
import pandas as pd #for manipulating the csv data
pd.set_option('display.max_rows', None)
import numpy as np #for mathematical calculation
from sklearn.metrics import confusion_matrix, accuracy_score, classification_report
from sklearn.model_selection import train_test_split
from math import log2, e, log, exp,log
import sys, threading
from sklearn.impute import KNNImputer
sys.setrecursionlimit(10**7) # max depth of recursion
threading.stack_size(2**27)  # new thread will get stack of such size

0


def nulling (df_in, col_name):
    for i,row in enumerate(df_in[col_name]):
        if row == 0:
            df_in.at[i,col_name]=None
            
    df_out = df_in
    return df_out

def rounding (df_in, col_name,r):
    for i,row in enumerate(df_in[col_name]):
        df_in.at[i,col_name]=round(row,r)
    df_out = df_in
    return df_out

def impute_knn(df_in):
    imputer = KNNImputer(n_neighbors=3)
    imputed = imputer.fit_transform(df_in)
    df_imputed = pd.DataFrame(imputed, columns=df_in.columns)
    return df_imputed

def impute_knnn(df_in):
    data = df_in
    imputer = KNNImputer(n_neighbors=3)
    #class 0
    class0 = data[data['Outcome']==0]
    class0idx = class0.index
    #class 1
    class1=data[data['Outcome']==1]
    class1idx = class1.index

    imputedc0 = imputer.fit_transform(class0)
    imputedc1 = imputer.fit_transform(class1)
    df_imputed0 = pd.DataFrame(imputedc0,index=class0idx, columns=data.columns)
    df_imputed1 = pd.DataFrame(imputedc1,index=class1idx, columns=data.columns)

    df_imputed=pd.concat([df_imputed0,df_imputed1])
    df_imputed=df_imputed.sort_index()
    return df_imputed
    
def praproses(daf,knn=1):
    df=daf.copy()
    tempn= ['Glucose','BloodPressure','SkinThickness','Insulin','BMI']
    tempr={
        'Glucose':0,
        'Pregnancies':0,
        'BloodPressure':0,
        'SkinThickness':0,
        'Insulin':0,
        'BMI':1
    }
    if knn == 1:
        for i in df.head():
            if i in tempn:
                df= nulling(df,i)
        df=impute_knnn(df)

    for i in df.head():
        if i in tempr:
            df=rounding(df,i,tempr[i])
    return df

def get_label_v(labels):
    n_labels = len(labels)
    if n_labels <= 1:
        return 0
    value,counts = np.unique(labels, return_counts=True)
    probs = counts / n_labels
    
    return value,counts,n_labels,probs

def entropy(probs):
    ent = 0.
    try:
        for i in probs:
            ent -= i * log2(i)
        return ent
    except:
        ent=0
        return ent

def ign_zero(x,y):
    try:
        return x/y
    except:
        return 0

def selecter(val,df,col):
    sk=len(df.loc[df[col]<=val])
    sl=len(df.loc[df[col]>val])
    sk0=len(df.loc[(df[col]<=val)&(df['Outcome']==0)].filter(col))
    sk1=len(df.loc[(df[col]<=val)&(df['Outcome']==1)].filter(col))
    sl0=len(df.loc[(df[col]>val)&(df['Outcome']==0)].filter(col))
    sl1=len(df.loc[(df[col]>val)&(df['Outcome']==1)].filter(col))
    pskt=[ign_zero(sk0,sk),ign_zero(sk1,sk)]
    pslt=[ign_zero(sl0,sl),ign_zero(sl1,sl)]
    
    return sk,sk0,sk1,sl,sl0,sl1,pskt,pslt

def get_max(df):
    minval =-1
    val=0
    for i in df:
        if i[1] > minval:
            minval=i[1]
            val=i[0]
    return minval,val
    
def diskritisasi(df,l_data,l_col):
    temp=[]
    value,counts,n_val,probs = get_label_v(df[l_col])
    for idx,i in enumerate(value):
        sk,_,_,sl,_,_,pskt,pslt = selecter(i,df,l_col)
        epskt=entropy(pskt)
        epslt=entropy(pslt)
        _,_,n_data,pdata= get_label_v(df[l_data])
        entt=entropy(pdata)
        informa= entt-(sk/n_data*epskt+sl/n_data*epslt)
        temp.append([i,informa])
    return get_max(temp)

def helper_disk(df,l_data):
    temp={}
    for i in df.head():
        if i != l_data:
            temp[i]=diskritisasi(df,l_data,i)
    return temp    

def tosplt(df,sp):
    zz=df
    for i in sp:
        aa= [sp[i][1] if x== True else sp[i][1]+1 for x in zz[i]<=sp[i][1] ]
        zz.drop(columns=[i])
        zz[i]=aa
    return zz

def runAll(dataa):
    data=dataa.copy()
    data=praproses(data)
    splitter = helper_disk(data,'Outcome')
    print(splitter)
    zz=data.copy()    
    pp_disc=tosplt(zz,splitter)

    return pp_disc

def runNoknn(dataa):
    data=dataa.copy()
    data=praproses(data,0)
    splitter = helper_disk(data,'Outcome')
    zz=data.copy()    
    pp_disc=tosplt(zz,splitter)

    return pp_disc


raw_data = pd.read_csv('diabetes.csv')
raw_data


datasets_imputed = praproses(raw_data)
datasets_imputed


dnoknn = runNoknn(raw_data)
dnoknn


dwithknn = runAll(raw_data)
dwithknn

{'Pregnancies': (0.039180260954807755, 6.0), 'Glucose': (0.13518841941127935, 127.0), 'BloodPressure': (0.018731718578645196, 63.0), 'SkinThickness': (0.07250945002996534, 23.0), 'Insulin': (0.14435516834581175, 108.0), 'BMI': (0.07469977982812326, 27.8), 'DiabetesPedigreeFunction': (0.02079639637723707, 0.527), 'Age': (0.0724727100056407, 28.0)}


def calc_total_entropy(train_data, label, class_list,t):
    total_row = train_data[t].sum() #the total size of the dataset
    total_entr = 0
    
    for c in class_list: #for each class in the label
        total_class_count = train_data[train_data[label] == c].sum()[t] #number of the class
        total_class_entr = - (total_class_count/total_row)*np.log2(total_class_count/total_row) #entropy of the class
        total_entr += total_class_entr #adding the class entropy to the total entropy of the dataset
    
    return total_entr

def calc_entropy(feature_value_data, label, class_list,t):
    class_count = feature_value_data[t].sum()
    entropy = 0
    
    for c in class_list:
        label_class_count = feature_value_data[feature_value_data[label] == c].sum()[t] #row count of class c 
        entropy_class = 0
        if label_class_count != 0:
            probability_class = label_class_count/class_count #probability of the class
            entropy_class = - probability_class * np.log2(probability_class)  #entropy
        entropy += entropy_class
    return entropy


def calc_info_gain(feature_name, train_data, label, class_list,t):
    feature_value_list = train_data[feature_name].unique() #unqiue values of the feature
    total_row = train_data[t].sum()
    feature_info = 0.0
    
    for feature_value in feature_value_list:
        feature_value_data = train_data[train_data[feature_name] == feature_value] #filtering rows with that feature_value
        feature_value_count = feature_value_data[t].sum()
        feature_value_entropy = calc_entropy(feature_value_data, label, class_list,t) #calculcating entropy for the feature value
        feature_value_probability = feature_value_count/total_row
        feature_info += feature_value_probability * feature_value_entropy #calculating information of the feature value
        
    return calc_total_entropy(train_data, label, class_list,t) - feature_info #calculating information gain by subtracting


def calc_sp_info(feature_name, train_data, label, class_list,t):
    feature_value_list = train_data[feature_name].unique() #unqiue values of the feature
    total_row = train_data[t].sum()
    feature_info = 0.0
    
    for feature_value in feature_value_list:
        feature_value_data = train_data[train_data[feature_name] == feature_value]
        feature_value_count = feature_value_data[t].sum()
        #feature_value_entropy = calc_entropy(feature_value_data, label, class_list)
        feature_value_probability = feature_value_count/total_row
        feature_info += -feature_value_probability * np.log2(feature_value_probability)
        
    return feature_info


def find_most_informative_feature(train_data, label, class_list,t):
    feature_list = ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction','Age']

    max_gn_rat = -1
    gn_rat = -1
    max_info_feature = None
    
    for feature in feature_list:  
        feature_info_gain = calc_info_gain(feature, train_data, label, class_list,t)
        feature_sp_info = calc_sp_info(feature, train_data, label, class_list,t)
        try:
            gn_rat=feature_info_gain.item()/feature_sp_info.item()
        except:
            gn_rat = -1
        if max_gn_rat < gn_rat:
            max_gn_rat = gn_rat
            max_info_feature = feature
    return max_info_feature


def generate_sub_tree(feature_name, train_data, label, class_list, t,cek=None):
    if feature_name==None:
        feature_name=cek
    xcv={}
    for tmp in train_data[feature_name].unique():
        ss=train_data[train_data[feature_name]==tmp].sum()[t]
        xcv[tmp]=ss

    feature_value_count_dict= pd.Series(xcv)
    f_left=8
    l_node=False

    cf= ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction','Age']
    for f in cf:
        panj = len(train_data[f].unique())
        if panj == 1:
            f_left-=1
    if f_left<=1:
        l_node=True
    tree = {} #sub tree or node
    cek_fitur = cek

    for feature_value, count in feature_value_count_dict.iteritems():
        feature_value_data = train_data[train_data[feature_name] == feature_value] #dataset with only feature_name = feature_value
        
        assigned_to_node = False #flag for tracking feature_value is pure class or not
        cek_count = []
        cek_class = []

        for cek in class_list:
            class_count = feature_value_data[feature_value_data[label] == cek].sum()[t] #count of class c
            cek_count.append(class_count)
            cek_class.append(cek)

        for c in class_list: #for each class
            class_count = feature_value_data[feature_value_data[label] == c].sum()[t] #count of class c

            if l_node ==True:
                if cek_count[1] > cek_count[0] :
                    tree[feature_value] = float(cek_class[1]) #adding node to the tree
                    train_data = train_data[train_data[feature_name] != feature_value] #removing rows with feature_value
                    assigned_to_node = True
                else : 
                    tree[feature_value] = float(cek_class[0]) #adding node to the tree
                    train_data = train_data[train_data[feature_name] != feature_value] #removing rows with feature_value
                    assigned_to_node = True
            elif cek_fitur != feature_name : #tambahan pengecekan
                if class_count == count: #count of feature_value = count of class (pure class)
                    tree[feature_value] = c #adding node to the tree
                    train_data = train_data[train_data[feature_name] != feature_value] #removing rows with feature_value
                    assigned_to_node = True
            elif cek_fitur == feature_name : #tambahan pengecekan
                if cek_count[1] > cek_count[0] :
                    tree[feature_value] = float(cek_class[1]) #adding node to the tree
                    train_data = train_data[train_data[feature_name] != feature_value] #removing rows with feature_value
                    assigned_to_node = True
                else : 
                    tree[feature_value] = float(cek_class[0]) #adding node to the tree
                    train_data = train_data[train_data[feature_name] != feature_value] #removing rows with feature_value
                    assigned_to_node = True
        if not assigned_to_node: #not pure class
            tree[feature_value] = "?" #should extend the node, so the branch is marked with ?

    return tree, train_data

def make_tree(root, prev_feature_value, train_data, label, class_list,t , cek_fitur=None):

    if train_data[t].sum() != 0: #if dataset becomes enpty after updating
        max_info_feature = find_most_informative_feature(train_data, label, class_list,t) #most informative feature
        tree, train_data = generate_sub_tree(max_info_feature, train_data, label, class_list,t, cek_fitur)
        next_root = None
        if max_info_feature == None :
            max_info_feature=cek_fitur
        if prev_feature_value != None: #add to intermediate node of the tree
            root[prev_feature_value] = dict()
            root[prev_feature_value][max_info_feature] = tree
            next_root = root[prev_feature_value][max_info_feature]
        else: #add to root of the tree
            root[max_info_feature] = tree
            next_root = root[max_info_feature]
        
        for node, branch in list(next_root.items()): #iterating the tree node
            if branch == "?": #if it is expandable
                feature_value_data = train_data[train_data[max_info_feature] == node] #using the updated dataset
                cek_fitur = max_info_feature
                make_tree(next_root, node, feature_value_data, label, class_list,t, cek_fitur) #recursive call with updated dataset



def generate_sub_stump(feature_name, train_data, label, class_list, t,cek=None):
    if feature_name==None:
        feature_name=cek

    xcv={}
    for tmp in train_data[feature_name].unique():
        ss=train_data[train_data[feature_name]==tmp].sum()[t]
        xcv[tmp]=ss

    feature_value_count_dict= pd.Series(xcv)
    f_left=8
    l_node=False

    cf= ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction','Age']
    for f in cf:
        panj = len(train_data[f].unique())
        if panj == 1:
            f_left-=1
    if f_left<=1:
        l_node=True
    tree = {}


    for feature_value, count in feature_value_count_dict.iteritems():

        feature_value_data = train_data[train_data[feature_name] == feature_value] 

        cek_count = []
        cek_class = []

        for cek in class_list:
            class_count = feature_value_data[feature_value_data[label] == cek].sum()[t] #count of class c
            cek_count.append(class_count)
            cek_class.append(cek)

        for c in class_list: #for each class
            class_count = feature_value_data[feature_value_data[label] == c].sum()[t]
            if cek_count[1] > cek_count[0] :
                    tree[feature_value] = float(cek_class[1]) 
            else : 
                tree[feature_value] = float(cek_class[0])
  
    return tree

def make_stump(root, prev_feature_value, train_data, label, class_list,t , cek_fitur=None):
    if train_data[t].sum() != 0:
        max_info_feature = find_most_informative_feature(train_data, label, class_list,t) #most informative feature
        tree = generate_sub_stump(max_info_feature, train_data, label, class_list,t, cek_fitur)
        root[max_info_feature] = tree

def c45(train_data_m, label,t):
    train_data = train_data_m.copy() #getting a copy of the dataset
    if type(t) == int :
        train_data,tn= weigher(train_data,t)
    else :
        tn=t
    tree = {} #tree which will be updated
    class_list = train_data[label].unique() #getting unqiue classes of the label
    make_tree(tree, None, train_data, label, class_list,tn) #start calling recursion
    return tree

def s_c45(train_data_m, label,t):
    train_data = train_data_m.copy() #getting a copy of the dataset
    tree = {} #tree which will be updated
    class_list = train_data[label].unique() #getting unqiue classes of the label
    #print("class list :", class_list)
    make_stump(tree, None, train_data_m, label, class_list,t) #start calling recursion
    return tree

def formatData(t,s):
    if not isinstance(t,dict) and not isinstance(t,list):
        print("\t"*s+str(t))
    else:
        for key in t:
            print("\t"*s+str(key))
            if not isinstance(t,list):
                formatData(t[key],s+1)


def predict(tree, instance):
    if not isinstance(tree, dict): #if it is leaf node
        return tree #return the value
    else:
        root_node = next(iter(tree)) #getting first key/feature name of the dictionary
        feature_value = instance[root_node] #value of the feature
        if feature_value in tree[root_node]: #checking the feature value in current tree node
            return predict(tree[root_node][feature_value], instance) #goto next feature
        else:
            return None

def evaluate(tree, test_data_m):
    hasil_prediksi = []
    for i in range (len(test_data_m)): #for each row in the dataset
        result = predict(tree, test_data_m.iloc[i]) #predict the row
        hasil_prediksi.append(result) #mendapatkan hasil prediksi dari data yang diuji

    return hasil_prediksi


def performa(y_test, y_pred):
    #confusion matrix
    matrix = confusion_matrix(y_test, y_pred, labels=[1,0])
    print('Confusion matrix : \n',matrix)
    print("")

    tp, fn, fp, tn = confusion_matrix(y_test, y_pred,labels=[1,0]).reshape(-1)
    print("True positive :", tp)
    print("False negative :", fn)
    print("False positive :", fp)
    print("True negative :", tn)
    print("")

    matrix = classification_report(y_test, y_pred,labels=[1,0])
    print('Classification report : \n',matrix)
    print("")

    #akurasi
    accuracy = round(accuracy_score(y_test, y_pred)*100, 2)
    presisi = round((tp/(tp+fp))*100, 2)
    recall = round((tp/(tp+fn))*100, 2)
    print("Akurasi fold ini adalah :", accuracy, " %")
    print("Presisi fold ini adalah :", presisi, " %")
    print("Recall fold ini adalah :", recall, " %")
    accuracy2 = round(((tp+tn)/(tp+fn+fp+tn)*100), 2)
    print("===========================================================")

    return accuracy, presisi, recall


def t_to(t):
    t=str(t)
    t='w'+t
    return t


def weigher(train_data,t,missc=None,alpha=None):
    ts=t_to(t)
    tsb=t_to(t-1)
    val = train_data.shape[0]
    if t == 0:
        zz = [1/val for _ in range(val)]
        train_data[ts]=zz
        return train_data,ts
    else :
        xx=[x for x in train_data[tsb]]
        train_data[ts]=xx
        for i in missc:
            nweight=train_data[ts][i]*exp(alpha)
            train_data.at[i,ts]=nweight
        norm = [x/train_data[ts].sum() for x in train_data[ts]]
        train_data[ts]=norm
        train_data=train_data.drop(columns=[tsb])
        
        return train_data,ts

def stump(tree,train_data,t):
    hp = evaluate(tree,train_data)
    missc=[]
    bennc=[]
    erate=0
    train_data['hp']=hp
    for i in train_data.index:
        if train_data['Outcome'][i] != train_data['hp'][i]:
            missc.append(i)
            erate+=train_data[t][i]
        else :
            bennc.append(i)
    alpha = log((1-erate)/erate)
    return missc,alpha

def adaboost(t,train_data):
    missc=0
    alpha=0
    hk=[]
    for i in range (t):
        train_data,tn = weigher(train_data,i,missc,alpha)
        tree=s_c45(train_data,'Outcome',tn)
        missc,alpha=stump(tree,train_data,tn)
        print(tn,tree,alpha)
        hk.append([tree,alpha])
    return hk

def predict_adb(tree,test_data_m,sk):
    pred=[]
    w_pred=[]
    for ij in range(len(test_data_m)):
        ppos=0
        pneg=0
        ttmp=[]
        for ji in range(sk):
            hk=predict(tree[ji][0],test_data_m.iloc[ij])
            ttmp.append(hk)
            if hk == 0:
                pneg+=tree[ji][1]
            elif hk == 1:
                ppos+=tree[ji][1]
        if ppos>pneg :
            hk= 1 
        elif ppos<pneg:
            hk = 0
        w_pred.append(ttmp)
        pred.append(hk)
    return pred


def k_fold(dataset,t,skn):
    polo=len(dataset)
    k=10
    fsize=round(polo/k)
    c45_final={}
    c45_final_acc =[]
    c45_final_pre=[]
    c45_final_rec=[]
    adab_final={}
    adab_final_acc=[]
    adab_final_pre=[]
    adab_final_rec=[]
    adatree=[]
    treec45s=[]

    for o in range(k):
        ada_sk=[]
        ada_pre=[]
        ada_rec=[]
        bak=round(polo/k)
        aa= 0 if o==0 else o+1
        bak= bak if aa==0 else bak*aa
        baw=bak-fsize 
        test_data = dataset[aa:bak] if aa == 0 else dataset[baw:bak]
        train_data = dataset[bak:] if aa == 0 else pd.concat([dataset[:baw],dataset[bak:]])

        y_test=test_data['Outcome']
        yyxx=[x for x in y_test]
        
        print(">>> FOLD KE ",o+1)
        print("Panjang data test :", test_data.shape[0])
        print("Panjang data train :", train_data.shape[0])
        print("Data Test :\n",yyxx)
        
        print("*******************")
        print("Algoritma C4.5")
        print("*******************")
        treec45=c45(train_data.copy(),'Outcome',0)

        c45_y_pred =evaluate(treec45,test_data)
        
        print("Hasil Prediksi C45 :\n", c45_y_pred)
        
        accuracy, presisi, recall = performa(y_test, c45_y_pred)
        c45_final_acc.append(accuracy)
        c45_final_pre.append(presisi)
        c45_final_rec.append(recall)

        treec45s.append(treec45)
        
        adahk=adaboost(t,train_data.copy())
        hsk=[]
        for sk in skn:
            hkk=predict_adb(adahk,test_data,sk)
            hsk.append(hkk)
            print("*******************")
            print("Adaboost ","t-",sk)
            print("*******************")
            print("Hasil Prediksi Adaboost :\n", hkk)
            ax1,ax2,ax3 = performa(y_test, hkk)
            ada_sk.append(ax1)
            ada_pre.append(ax2)
            ada_rec.append(ax3)

        adatree.append(adahk)
        adab_final_acc.append(ada_sk)
        adab_final_pre.append(ada_pre)
        adab_final_rec.append(ada_rec)
    
    c45_final['akurasi']=c45_final_acc
    c45_final['presisi']=c45_final_pre
    c45_final['recall']=c45_final_rec

    adab_final['akurasi']=adab_final_acc
    adab_final['presisi']=adab_final_pre
    adab_final['recall']=adab_final_rec
    
    return c45_final,adab_final,adatree,treec45s


sknar=[5,10,15,20]
t=20
wk_acc_c45,wk_acc_adb,adatree,treec45s=k_fold(dwithknn,t,sknar)

>>> FOLD KE  1
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0]
Confusion matrix : 
 [[15 17]
 [11 34]]

True positive : 15
False negative : 17
False positive : 11
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.58      0.47      0.52        32
           0       0.67      0.76      0.71        45

    accuracy                           0.64        77
   macro avg       0.62      0.61      0.61        77
weighted avg       0.63      0.64      0.63        77


Akurasi fold ini adalah : 63.64  %
Presisi fold ini adalah : 57.69  %
Recall fold ini adalah : 46.88  %
===========================================================
w0 {'Glucose': {127.0: 0.0, 128.0: 1.0}} 1.10440940635244
w1 {'Insulin': {108.0: 0.0, 109.0: 0.0}} 0.5105149608566384
w2 {'Insulin': {108.0: 0.0, 109.0: 1.0}} 0.5827071946525647
w3 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.29741316767195075
w4 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.4616143510693342
w5 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.3903193823112505
w6 {'Age': {28.0: 0.0, 29.0: 1.0}} 0.4078434627714327
w7 {'DiabetesPedigreeFunction': {0.527: 0.0, 1.5270000000000001: 1.0}} 0.402934904789061
w8 {'Glucose': {127.0: 1.0, 128.0: 0.0}} 0.3265825744284367
w9 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.21222065930141987
w10 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.17014242126889767
w11 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.22458853671758877
w12 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.12141524726088349
w13 {'Insulin': {108.0: 0.0, 109.0: 0.0}} 0.050689493098667965
w14 {'Insulin': {108.0: 0.0, 109.0: 1.0}} 0.07207070344663198
w15 {'BloodPressure': {64.0: 0.0, 63.0: 1.0}} 0.06056865284921031
w16 {'DiabetesPedigreeFunction': {0.527: 1.0, 1.5270000000000001: 0.0}} 0.0489943889764297
w17 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.028790608426090183
w18 {'Glucose': {127.0: 1.0, 128.0: 0.0}} 0.03669136337045756
w19 {'Insulin': {108.0: 0.0, 109.0: 0.0}} 0.030320755499146708
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[16 16]
 [11 34]]

True positive : 16
False negative : 16
False positive : 11
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.59      0.50      0.54        32
           0       0.68      0.76      0.72        45

    accuracy                           0.65        77
   macro avg       0.64      0.63      0.63        77
weighted avg       0.64      0.65      0.64        77


Akurasi fold ini adalah : 64.94  %
Presisi fold ini adalah : 59.26  %
Recall fold ini adalah : 50.0  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[21 11]
 [12 33]]

True positive : 21
False negative : 11
False positive : 12
True negative : 33

Classification report : 
               precision    recall  f1-score   support

           1       0.64      0.66      0.65        32
           0       0.75      0.73      0.74        45

    accuracy                           0.70        77
   macro avg       0.69      0.69      0.69        77
weighted avg       0.70      0.70      0.70        77


Akurasi fold ini adalah : 70.13  %
Presisi fold ini adalah : 63.64  %
Recall fold ini adalah : 65.62  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[20 12]
 [11 34]]

True positive : 20
False negative : 12
False positive : 11
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.65      0.62      0.63        32
           0       0.74      0.76      0.75        45

    accuracy                           0.70        77
   macro avg       0.69      0.69      0.69        77
weighted avg       0.70      0.70      0.70        77


Akurasi fold ini adalah : 70.13  %
Presisi fold ini adalah : 64.52  %
Recall fold ini adalah : 62.5  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[20 12]
 [11 34]]

True positive : 20
False negative : 12
False positive : 11
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.65      0.62      0.63        32
           0       0.74      0.76      0.75        45

    accuracy                           0.70        77
   macro avg       0.69      0.69      0.69        77
weighted avg       0.70      0.70      0.70        77


Akurasi fold ini adalah : 70.13  %
Presisi fold ini adalah : 64.52  %
Recall fold ini adalah : 62.5  %
===========================================================
>>> FOLD KE  2
Panjang data test : 77
Panjang data train : 691
Data Test :
 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0]
Confusion matrix : 
 [[15  7]
 [13 42]]

True positive : 15
False negative : 7
False positive : 13
True negative : 42

Classification report : 
               precision    recall  f1-score   support

           1       0.54      0.68      0.60        22
           0       0.86      0.76      0.81        55

    accuracy                           0.74        77
   macro avg       0.70      0.72      0.70        77
weighted avg       0.77      0.74      0.75        77


Akurasi fold ini adalah : 74.03  %
Presisi fold ini adalah : 53.57  %
Recall fold ini adalah : 68.18  %
===========================================================
w0 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.656465613925347
w1 {'Glucose': {128.0: 0.0, 127.0: 0.0}} 0.8770774997129767
w2 {'Glucose': {128.0: 1.0, 127.0: 0.0}} 0.752291949469069
w3 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5482750124497324
w4 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.43787527014685307
w5 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.32377557296169585
w6 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.26238039737868124
w7 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.35619679374851937
w8 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.22093034541067996
w9 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.10849171802954947
w10 {'Glucose': {128.0: 0.0, 127.0: 1.0}} 0.085174653650404
w11 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.04030802250558255
w12 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.05494529081822132
w13 {'BloodPressure': {64.0: 0.0, 63.0: 1.0}} 0.0661801615781846
w14 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.03524132600835429
w15 {'Age': {29.0: 0.0, 28.0: 1.0}} 0.03698085925431426
w16 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.032721275417328506
w17 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.018475417449789967
w18 {'BloodPressure': {64.0: 0.0, 63.0: 1.0}} 0.009903989354940862
w19 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.014493390376846634
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1]
Confusion matrix : 
 [[17  5]
 [15 40]]

True positive : 17
False negative : 5
False positive : 15
True negative : 40

Classification report : 
               precision    recall  f1-score   support

           1       0.53      0.77      0.63        22
           0       0.89      0.73      0.80        55

    accuracy                           0.74        77
   macro avg       0.71      0.75      0.71        77
weighted avg       0.79      0.74      0.75        77


Akurasi fold ini adalah : 74.03  %
Presisi fold ini adalah : 53.12  %
Recall fold ini adalah : 77.27  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1]
Confusion matrix : 
 [[13  9]
 [ 8 47]]

True positive : 13
False negative : 9
False positive : 8
True negative : 47

Classification report : 
               precision    recall  f1-score   support

           1       0.62      0.59      0.60        22
           0       0.84      0.85      0.85        55

    accuracy                           0.78        77
   macro avg       0.73      0.72      0.73        77
weighted avg       0.78      0.78      0.78        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 61.9  %
Recall fold ini adalah : 59.09  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1]
Confusion matrix : 
 [[13  9]
 [ 8 47]]

True positive : 13
False negative : 9
False positive : 8
True negative : 47

Classification report : 
               precision    recall  f1-score   support

           1       0.62      0.59      0.60        22
           0       0.84      0.85      0.85        55

    accuracy                           0.78        77
   macro avg       0.73      0.72      0.73        77
weighted avg       0.78      0.78      0.78        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 61.9  %
Recall fold ini adalah : 59.09  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1]
Confusion matrix : 
 [[14  8]
 [ 9 46]]

True positive : 14
False negative : 8
False positive : 9
True negative : 46

Classification report : 
               precision    recall  f1-score   support

           1       0.61      0.64      0.62        22
           0       0.85      0.84      0.84        55

    accuracy                           0.78        77
   macro avg       0.73      0.74      0.73        77
weighted avg       0.78      0.78      0.78        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 60.87  %
Recall fold ini adalah : 63.64  %
===========================================================
>>> FOLD KE  3
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0]
Confusion matrix : 
 [[29  5]
 [ 9 34]]

True positive : 29
False negative : 5
False positive : 9
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.76      0.85      0.81        34
           0       0.87      0.79      0.83        43

    accuracy                           0.82        77
   macro avg       0.82      0.82      0.82        77
weighted avg       0.82      0.82      0.82        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 76.32  %
Recall fold ini adalah : 85.29  %
===========================================================
w0 {'Insulin': {109.0: 0.0, 108.0: 0.0}} 0.6693622755365096
w1 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.9070401171457856
w2 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.38845229291061584
w3 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5190140002520912
w4 {'Glucose': {128.0: 1.0, 127.0: 0.0}} 0.6644931834435341
w5 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.22513797598828117
w6 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.3593521807914274
w7 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.40561019719745184
w8 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.3301917125686123
w9 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.24027833530577264
w10 {'Insulin': {109.0: 0.0, 108.0: 1.0}} 0.21433400070772435
w11 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.1444724689548544
w12 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.0453125646710913
w13 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.0735094596457429
w14 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.04662997658070943
w15 {'Pregnancies': {6.0: 0.0, 7.0: 0.0}} 0.029600092344392103
w16 {'Pregnancies': {6.0: 1.0, 7.0: 0.0}} 0.044227829143850296
w17 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.022778329084824703
w18 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.021653205777851466
w19 {'Insulin': {109.0: 0.0, 108.0: 1.0}} 0.03300982325983914
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1]
Confusion matrix : 
 [[24 10]
 [ 8 35]]

True positive : 24
False negative : 10
False positive : 8
True negative : 35

Classification report : 
               precision    recall  f1-score   support

           1       0.75      0.71      0.73        34
           0       0.78      0.81      0.80        43

    accuracy                           0.77        77
   macro avg       0.76      0.76      0.76        77
weighted avg       0.77      0.77      0.77        77


Akurasi fold ini adalah : 76.62  %
Presisi fold ini adalah : 75.0  %
Recall fold ini adalah : 70.59  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1]
Confusion matrix : 
 [[26  8]
 [ 9 34]]

True positive : 26
False negative : 8
False positive : 9
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.74      0.76      0.75        34
           0       0.81      0.79      0.80        43

    accuracy                           0.78        77
   macro avg       0.78      0.78      0.78        77
weighted avg       0.78      0.78      0.78        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 74.29  %
Recall fold ini adalah : 76.47  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1]
Confusion matrix : 
 [[27  7]
 [ 9 34]]

True positive : 27
False negative : 7
False positive : 9
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.75      0.79      0.77        34
           0       0.83      0.79      0.81        43

    accuracy                           0.79        77
   macro avg       0.79      0.79      0.79        77
weighted avg       0.79      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 75.0  %
Recall fold ini adalah : 79.41  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1]
Confusion matrix : 
 [[27  7]
 [ 9 34]]

True positive : 27
False negative : 7
False positive : 9
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.75      0.79      0.77        34
           0       0.83      0.79      0.81        43

    accuracy                           0.79        77
   macro avg       0.79      0.79      0.79        77
weighted avg       0.79      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 75.0  %
Recall fold ini adalah : 79.41  %
===========================================================
>>> FOLD KE  4
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Confusion matrix : 
 [[19 11]
 [14 33]]

True positive : 19
False negative : 11
False positive : 14
True negative : 33

Classification report : 
               precision    recall  f1-score   support

           1       0.58      0.63      0.60        30
           0       0.75      0.70      0.73        47

    accuracy                           0.68        77
   macro avg       0.66      0.67      0.66        77
weighted avg       0.68      0.68      0.68        77


Akurasi fold ini adalah : 67.53  %
Presisi fold ini adalah : 57.58  %
Recall fold ini adalah : 63.33  %
===========================================================
w0 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.6693622755365096
w1 {'Glucose': {128.0: 0.0, 127.0: 0.0}} 0.9177443632614095
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5843608162804057
w3 {'Glucose': {128.0: 1.0, 127.0: 0.0}} 0.8065845870110339
w4 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.46271971252002264
w5 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.3501036828870159
w6 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.2710024271191263
w7 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.2994537240308235
w8 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.2155806392744649
w9 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.14988449170255355
w10 {'Glucose': {128.0: 0.0, 127.0: 1.0}} 0.10945813699346084
w11 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.08553936511998017
w12 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.05655937407307682
w13 {'BloodPressure': {64.0: 0.0, 63.0: 0.0}} 0.035805415554311285
w14 {'BloodPressure': {64.0: 1.0, 63.0: 0.0}} 0.04411214664607982
w15 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.029695009057984542
w16 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.033959455747984674
w17 {'Age': {29.0: 0.0, 28.0: 1.0}} 0.05337244794106118
w18 {'BloodPressure': {64.0: 1.0, 63.0: 0.0}} 0.018261596716922898
w19 {'Insulin': {109.0: 0.0, 108.0: 1.0}} 0.03884950184349952
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[17 13]
 [13 34]]

True positive : 17
False negative : 13
False positive : 13
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.57      0.57      0.57        30
           0       0.72      0.72      0.72        47

    accuracy                           0.66        77
   macro avg       0.65      0.65      0.65        77
weighted avg       0.66      0.66      0.66        77


Akurasi fold ini adalah : 66.23  %
Presisi fold ini adalah : 56.67  %
Recall fold ini adalah : 56.67  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[18 12]
 [11 36]]

True positive : 18
False negative : 12
False positive : 11
True negative : 36

Classification report : 
               precision    recall  f1-score   support

           1       0.62      0.60      0.61        30
           0       0.75      0.77      0.76        47

    accuracy                           0.70        77
   macro avg       0.69      0.68      0.68        77
weighted avg       0.70      0.70      0.70        77


Akurasi fold ini adalah : 70.13  %
Presisi fold ini adalah : 62.07  %
Recall fold ini adalah : 60.0  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[19 11]
 [14 33]]

True positive : 19
False negative : 11
False positive : 14
True negative : 33

Classification report : 
               precision    recall  f1-score   support

           1       0.58      0.63      0.60        30
           0       0.75      0.70      0.73        47

    accuracy                           0.68        77
   macro avg       0.66      0.67      0.66        77
weighted avg       0.68      0.68      0.68        77


Akurasi fold ini adalah : 67.53  %
Presisi fold ini adalah : 57.58  %
Recall fold ini adalah : 63.33  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[18 12]
 [11 36]]

True positive : 18
False negative : 12
False positive : 11
True negative : 36

Classification report : 
               precision    recall  f1-score   support

           1       0.62      0.60      0.61        30
           0       0.75      0.77      0.76        47

    accuracy                           0.70        77
   macro avg       0.69      0.68      0.68        77
weighted avg       0.70      0.70      0.70        77


Akurasi fold ini adalah : 70.13  %
Presisi fold ini adalah : 62.07  %
Recall fold ini adalah : 60.0  %
===========================================================
>>> FOLD KE  5
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Confusion matrix : 
 [[17 10]
 [ 7 43]]

True positive : 17
False negative : 10
False positive : 7
True negative : 43

Classification report : 
               precision    recall  f1-score   support

           1       0.71      0.63      0.67        27
           0       0.81      0.86      0.83        50

    accuracy                           0.78        77
   macro avg       0.76      0.74      0.75        77
weighted avg       0.78      0.78      0.78        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 70.83  %
Recall fold ini adalah : 62.96  %
===========================================================
w0 {'Insulin': {109.0: 0.0, 108.0: 0.0}} 0.6244506492737171
w1 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.8856189292844829
w2 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.3921217836461866
w3 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5693456405008482
w4 {'Glucose': {128.0: 1.0, 127.0: 0.0}} 0.6492692790391739
w5 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.5064497287467298
w6 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.22364286277661025
w7 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.34424087200730547
w8 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.1025893505028567
w9 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.20310321899779077
w10 {'Insulin': {109.0: 0.0, 108.0: 1.0}} 0.24724250767354644
w11 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.06574303933826153
w12 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.1005737729730464
w13 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.08570070992150383
w14 {'BloodPressure': {64.0: 0.0, 63.0: 1.0}} 0.10007025347203398
w15 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.10300179463707708
w16 {'Pregnancies': {6.0: 1.0, 7.0: 0.0}} 0.1008831739165547
w17 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.06516827679038725
w18 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.060553514307021605
w19 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.039346752732509066
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[15 12]
 [ 5 45]]

True positive : 15
False negative : 12
False positive : 5
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.75      0.56      0.64        27
           0       0.79      0.90      0.84        50

    accuracy                           0.78        77
   macro avg       0.77      0.73      0.74        77
weighted avg       0.78      0.78      0.77        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 75.0  %
Recall fold ini adalah : 55.56  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[18  9]
 [ 7 43]]

True positive : 18
False negative : 9
False positive : 7
True negative : 43

Classification report : 
               precision    recall  f1-score   support

           1       0.72      0.67      0.69        27
           0       0.83      0.86      0.84        50

    accuracy                           0.79        77
   macro avg       0.77      0.76      0.77        77
weighted avg       0.79      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 72.0  %
Recall fold ini adalah : 66.67  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[16 11]
 [ 5 45]]

True positive : 16
False negative : 11
False positive : 5
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.76      0.59      0.67        27
           0       0.80      0.90      0.85        50

    accuracy                           0.79        77
   macro avg       0.78      0.75      0.76        77
weighted avg       0.79      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 76.19  %
Recall fold ini adalah : 59.26  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[18  9]
 [ 6 44]]

True positive : 18
False negative : 9
False positive : 6
True negative : 44

Classification report : 
               precision    recall  f1-score   support

           1       0.75      0.67      0.71        27
           0       0.83      0.88      0.85        50

    accuracy                           0.81        77
   macro avg       0.79      0.77      0.78        77
weighted avg       0.80      0.81      0.80        77


Akurasi fold ini adalah : 80.52  %
Presisi fold ini adalah : 75.0  %
Recall fold ini adalah : 66.67  %
===========================================================
>>> FOLD KE  6
Panjang data test : 77
Panjang data train : 691
Data Test :
 [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
Confusion matrix : 
 [[23  7]
 [ 7 40]]

True positive : 23
False negative : 7
False positive : 7
True negative : 40

Classification report : 
               precision    recall  f1-score   support

           1       0.77      0.77      0.77        30
           0       0.85      0.85      0.85        47

    accuracy                           0.82        77
   macro avg       0.81      0.81      0.81        77
weighted avg       0.82      0.82      0.82        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 76.67  %
Recall fold ini adalah : 76.67  %
===========================================================
w0 {'Insulin': {109.0: 0.0, 108.0: 0.0}} 0.6436214518115658
w1 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.9059286833114396
w2 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.40107702532169026
w3 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.4938270047430686
w4 {'Glucose': {128.0: 1.0, 127.0: 0.0}} 0.6364785793411107
w5 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.4785484130245446
w6 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.32598154922019473
w7 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.30252119812220085
w8 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.18685207137228102
w9 {'Insulin': {109.0: 0.0, 108.0: 1.0}} 0.20861064809505306
w10 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.06728818572197127
w11 {'Pregnancies': {6.0: 0.0, 7.0: 0.0}} 0.056697121642894865
w12 {'Pregnancies': {6.0: 1.0, 7.0: 0.0}} 0.06640968725853895
w13 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.09355066170646585
w14 {'Pregnancies': {6.0: 0.0, 7.0: 0.0}} 0.05272919994322677
w15 {'Pregnancies': {6.0: 1.0, 7.0: 0.0}} 0.05225745996822638
w16 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.02726091327424862
w17 {'BloodPressure': {64.0: 0.0, 63.0: 1.0}} 0.05348373898113194
w18 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.026558851120298748
w19 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.033970549304723624
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[20 10]
 [ 7 40]]

True positive : 20
False negative : 10
False positive : 7
True negative : 40

Classification report : 
               precision    recall  f1-score   support

           1       0.74      0.67      0.70        30
           0       0.80      0.85      0.82        47

    accuracy                           0.78        77
   macro avg       0.77      0.76      0.76        77
weighted avg       0.78      0.78      0.78        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 74.07  %
Recall fold ini adalah : 66.67  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[21  9]
 [ 5 42]]

True positive : 21
False negative : 9
False positive : 5
True negative : 42

Classification report : 
               precision    recall  f1-score   support

           1       0.81      0.70      0.75        30
           0       0.82      0.89      0.86        47

    accuracy                           0.82        77
   macro avg       0.82      0.80      0.80        77
weighted avg       0.82      0.82      0.82        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 80.77  %
Recall fold ini adalah : 70.0  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[21  9]
 [ 5 42]]

True positive : 21
False negative : 9
False positive : 5
True negative : 42

Classification report : 
               precision    recall  f1-score   support

           1       0.81      0.70      0.75        30
           0       0.82      0.89      0.86        47

    accuracy                           0.82        77
   macro avg       0.82      0.80      0.80        77
weighted avg       0.82      0.82      0.82        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 80.77  %
Recall fold ini adalah : 70.0  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[22  8]
 [ 6 41]]

True positive : 22
False negative : 8
False positive : 6
True negative : 41

Classification report : 
               precision    recall  f1-score   support

           1       0.79      0.73      0.76        30
           0       0.84      0.87      0.85        47

    accuracy                           0.82        77
   macro avg       0.81      0.80      0.81        77
weighted avg       0.82      0.82      0.82        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 78.57  %
Recall fold ini adalah : 73.33  %
===========================================================
>>> FOLD KE  7
Panjang data test : 77
Panjang data train : 691
Data Test :
 [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]
Confusion matrix : 
 [[11  3]
 [14 49]]

True positive : 11
False negative : 3
False positive : 14
True negative : 49

Classification report : 
               precision    recall  f1-score   support

           1       0.44      0.79      0.56        14
           0       0.94      0.78      0.85        63

    accuracy                           0.78        77
   macro avg       0.69      0.78      0.71        77
weighted avg       0.85      0.78      0.80        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 44.0  %
Recall fold ini adalah : 78.57  %
===========================================================
w0 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.6888087789613535
w1 {'Glucose': {128.0: 0.0, 127.0: 0.0}} 0.8329314022659339
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5201611145238069
w3 {'Glucose': {128.0: 1.0, 127.0: 0.0}} 0.7319623588581877
w4 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.21831868884815245
w5 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.3437521512876085
w6 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.3978778800754579
w7 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.41825012276803114
w8 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.2707586470384025
w9 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.07221079157942908
w10 {'Glucose': {128.0: 0.0, 127.0: 1.0}} 0.09593780719413367
w11 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.08281102508924519
w12 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.0495975946150453
w13 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.031242043390614455
w14 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.03636207163091169
w15 {'BloodPressure': {64.0: 0.0, 63.0: 1.0}} 0.05465969245073296
w16 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.043454268804588345
w17 {'Insulin': {109.0: 0.0, 108.0: 1.0}} 0.03253435571037753
w18 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.03304134886877499
w19 {'Age': {29.0: 0.0, 28.0: 1.0}} 0.027492320414903387
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[ 7  7]
 [ 9 54]]

True positive : 7
False negative : 7
False positive : 9
True negative : 54

Classification report : 
               precision    recall  f1-score   support

           1       0.44      0.50      0.47        14
           0       0.89      0.86      0.87        63

    accuracy                           0.79        77
   macro avg       0.66      0.68      0.67        77
weighted avg       0.80      0.79      0.80        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 43.75  %
Recall fold ini adalah : 50.0  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[11  3]
 [11 52]]

True positive : 11
False negative : 3
False positive : 11
True negative : 52

Classification report : 
               precision    recall  f1-score   support

           1       0.50      0.79      0.61        14
           0       0.95      0.83      0.88        63

    accuracy                           0.82        77
   macro avg       0.72      0.81      0.75        77
weighted avg       0.86      0.82      0.83        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 50.0  %
Recall fold ini adalah : 78.57  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[11  3]
 [11 52]]

True positive : 11
False negative : 3
False positive : 11
True negative : 52

Classification report : 
               precision    recall  f1-score   support

           1       0.50      0.79      0.61        14
           0       0.95      0.83      0.88        63

    accuracy                           0.82        77
   macro avg       0.72      0.81      0.75        77
weighted avg       0.86      0.82      0.83        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 50.0  %
Recall fold ini adalah : 78.57  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[11  3]
 [11 52]]

True positive : 11
False negative : 3
False positive : 11
True negative : 52

Classification report : 
               precision    recall  f1-score   support

           1       0.50      0.79      0.61        14
           0       0.95      0.83      0.88        63

    accuracy                           0.82        77
   macro avg       0.72      0.81      0.75        77
weighted avg       0.86      0.82      0.83        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 50.0  %
Recall fold ini adalah : 78.57  %
===========================================================
>>> FOLD KE  8
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0]
Confusion matrix : 
 [[22  3]
 [11 41]]

True positive : 22
False negative : 3
False positive : 11
True negative : 41

Classification report : 
               precision    recall  f1-score   support

           1       0.67      0.88      0.76        25
           0       0.93      0.79      0.85        52

    accuracy                           0.82        77
   macro avg       0.80      0.83      0.81        77
weighted avg       0.85      0.82      0.82        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 66.67  %
Recall fold ini adalah : 88.0  %
===========================================================
w0 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.6693622755365096
w1 {'Glucose': {128.0: 0.0, 127.0: 0.0}} 0.8759576212179595
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.505686602795264
w3 {'Glucose': {128.0: 1.0, 127.0: 0.0}} 0.7010801827725435
w4 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.4175266845453082
w5 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.29363682293990273
w6 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.28302621374581216
w7 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.2930490862918666
w8 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.2164413429329435
w9 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.1079923085953868
w10 {'Glucose': {128.0: 0.0, 127.0: 1.0}} 0.0915749922129381
w11 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.05786855279972085
w12 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.05748682301366659
w13 {'BloodPressure': {64.0: 0.0, 63.0: 1.0}} 0.06690714217723091
w14 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.034367688221293866
w15 {'Insulin': {109.0: 0.0, 108.0: 1.0}} 0.0433434162966958
w16 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.02608286857135777
w17 {'Age': {29.0: 0.0, 28.0: 1.0}} 0.02493713868162798
w18 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.02440038848666421
w19 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.02010147537943155
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
Confusion matrix : 
 [[24  1]
 [11 41]]

True positive : 24
False negative : 1
False positive : 11
True negative : 41

Classification report : 
               precision    recall  f1-score   support

           1       0.69      0.96      0.80        25
           0       0.98      0.79      0.87        52

    accuracy                           0.84        77
   macro avg       0.83      0.87      0.84        77
weighted avg       0.88      0.84      0.85        77


Akurasi fold ini adalah : 84.42  %
Presisi fold ini adalah : 68.57  %
Recall fold ini adalah : 96.0  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
Confusion matrix : 
 [[21  4]
 [ 7 45]]

True positive : 21
False negative : 4
False positive : 7
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.75      0.84      0.79        25
           0       0.92      0.87      0.89        52

    accuracy                           0.86        77
   macro avg       0.83      0.85      0.84        77
weighted avg       0.86      0.86      0.86        77


Akurasi fold ini adalah : 85.71  %
Presisi fold ini adalah : 75.0  %
Recall fold ini adalah : 84.0  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
Confusion matrix : 
 [[21  4]
 [ 7 45]]

True positive : 21
False negative : 4
False positive : 7
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.75      0.84      0.79        25
           0       0.92      0.87      0.89        52

    accuracy                           0.86        77
   macro avg       0.83      0.85      0.84        77
weighted avg       0.86      0.86      0.86        77


Akurasi fold ini adalah : 85.71  %
Presisi fold ini adalah : 75.0  %
Recall fold ini adalah : 84.0  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
Confusion matrix : 
 [[21  4]
 [ 7 45]]

True positive : 21
False negative : 4
False positive : 7
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.75      0.84      0.79        25
           0       0.92      0.87      0.89        52

    accuracy                           0.86        77
   macro avg       0.83      0.85      0.84        77
weighted avg       0.86      0.86      0.86        77


Akurasi fold ini adalah : 85.71  %
Presisi fold ini adalah : 75.0  %
Recall fold ini adalah : 84.0  %
===========================================================
>>> FOLD KE  9
Panjang data test : 77
Panjang data train : 691
Data Test :
 [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0]
Confusion matrix : 
 [[14 10]
 [13 40]]

True positive : 14
False negative : 10
False positive : 13
True negative : 40

Classification report : 
               precision    recall  f1-score   support

           1       0.52      0.58      0.55        24
           0       0.80      0.75      0.78        53

    accuracy                           0.70        77
   macro avg       0.66      0.67      0.66        77
weighted avg       0.71      0.70      0.71        77


Akurasi fold ini adalah : 70.13  %
Presisi fold ini adalah : 51.85  %
Recall fold ini adalah : 58.33  %
===========================================================
w0 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.6823128383942417
w1 {'Glucose': {128.0: 0.0, 127.0: 0.0}} 0.8835561392906363
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5500824827202888
w3 {'Glucose': {128.0: 1.0, 127.0: 0.0}} 0.7625985183825795
w4 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.42188890310518035
w5 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.3307855136714821
w6 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.2792913260911633
w7 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.3668689375474824
w8 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.17932899057657858
w9 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.11469237122000214
w10 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.050063828550624107
w11 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.07164876317059546
w12 {'Insulin': {109.0: 0.0, 108.0: 1.0}} 0.1040988716615704
w13 {'Glucose': {128.0: 0.0, 127.0: 1.0}} 0.06424878610093913
w14 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.03395835112016343
w15 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.021236701233633866
w16 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.017527072246665158
w17 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.011218774909028182
w18 {'BloodPressure': {64.0: 0.0, 63.0: 1.0}} 0.022364240282909672
w19 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.015818639002555184
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[17  7]
 [15 38]]

True positive : 17
False negative : 7
False positive : 15
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.53      0.71      0.61        24
           0       0.84      0.72      0.78        53

    accuracy                           0.71        77
   macro avg       0.69      0.71      0.69        77
weighted avg       0.75      0.71      0.72        77


Akurasi fold ini adalah : 71.43  %
Presisi fold ini adalah : 53.12  %
Recall fold ini adalah : 70.83  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[14 10]
 [11 42]]

True positive : 14
False negative : 10
False positive : 11
True negative : 42

Classification report : 
               precision    recall  f1-score   support

           1       0.56      0.58      0.57        24
           0       0.81      0.79      0.80        53

    accuracy                           0.73        77
   macro avg       0.68      0.69      0.69        77
weighted avg       0.73      0.73      0.73        77


Akurasi fold ini adalah : 72.73  %
Presisi fold ini adalah : 56.0  %
Recall fold ini adalah : 58.33  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[13 11]
 [10 43]]

True positive : 13
False negative : 11
False positive : 10
True negative : 43

Classification report : 
               precision    recall  f1-score   support

           1       0.57      0.54      0.55        24
           0       0.80      0.81      0.80        53

    accuracy                           0.73        77
   macro avg       0.68      0.68      0.68        77
weighted avg       0.72      0.73      0.73        77


Akurasi fold ini adalah : 72.73  %
Presisi fold ini adalah : 56.52  %
Recall fold ini adalah : 54.17  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[13 11]
 [10 43]]

True positive : 13
False negative : 11
False positive : 10
True negative : 43

Classification report : 
               precision    recall  f1-score   support

           1       0.57      0.54      0.55        24
           0       0.80      0.81      0.80        53

    accuracy                           0.73        77
   macro avg       0.68      0.68      0.68        77
weighted avg       0.72      0.73      0.73        77


Akurasi fold ini adalah : 72.73  %
Presisi fold ini adalah : 56.52  %
Recall fold ini adalah : 54.17  %
===========================================================
>>> FOLD KE  10
Panjang data test : 75
Panjang data train : 693
Data Test :
 [1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0]
Confusion matrix : 
 [[22  8]
 [ 6 39]]

True positive : 22
False negative : 8
False positive : 6
True negative : 39

Classification report : 
               precision    recall  f1-score   support

           1       0.79      0.73      0.76        30
           0       0.83      0.87      0.85        45

    accuracy                           0.81        75
   macro avg       0.81      0.80      0.80        75
weighted avg       0.81      0.81      0.81        75


Akurasi fold ini adalah : 81.33  %
Presisi fold ini adalah : 78.57  %
Recall fold ini adalah : 73.33  %
===========================================================
w0 {'Insulin': {109.0: 1.0, 108.0: 0.0}} 0.660851585868589
w1 {'Glucose': {128.0: 0.0, 127.0: 0.0}} 0.9098417100901979
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5671360924694835
w3 {'Glucose': {128.0: 1.0, 127.0: 0.0}} 0.6995240111822959
w4 {'Pregnancies': {6.0: 0.0, 7.0: 1.0}} 0.49968860121553443
w5 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.2834307764954357
w6 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.3370280832596378
w7 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.21038953803860658
w8 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.11839518543185859
w9 {'SkinThickness': {24.0: 1.0, 23.0: 0.0}} 0.09815814809742371
w10 {'Pregnancies': {6.0: 1.0, 7.0: 0.0}} 0.06740271753153945
w11 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.09653853411314195
w12 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.06509853386142073
w13 {'Glucose': {128.0: 0.0, 127.0: 1.0}} 0.07740453056313128
w14 {'Pregnancies': {6.0: 0.0, 7.0: 0.0}} 0.04434334187970054
w15 {'Pregnancies': {6.0: 1.0, 7.0: 0.0}} 0.047024942047195886
w16 {'Age': {29.0: 1.0, 28.0: 0.0}} 0.0612240284178165
w17 {'Pregnancies': {6.0: 0.0, 7.0: 0.0}} 0.036233317715199304
w18 {'Pregnancies': {6.0: 1.0, 7.0: 0.0}} 0.03626821358670146
w19 {'SkinThickness': {24.0: 0.0, 23.0: 0.0}} 0.019201534471161238
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[22  8]
 [ 6 39]]

True positive : 22
False negative : 8
False positive : 6
True negative : 39

Classification report : 
               precision    recall  f1-score   support

           1       0.79      0.73      0.76        30
           0       0.83      0.87      0.85        45

    accuracy                           0.81        75
   macro avg       0.81      0.80      0.80        75
weighted avg       0.81      0.81      0.81        75


Akurasi fold ini adalah : 81.33  %
Presisi fold ini adalah : 78.57  %
Recall fold ini adalah : 73.33  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[19 11]
 [ 5 40]]

True positive : 19
False negative : 11
False positive : 5
True negative : 40

Classification report : 
               precision    recall  f1-score   support

           1       0.79      0.63      0.70        30
           0       0.78      0.89      0.83        45

    accuracy                           0.79        75
   macro avg       0.79      0.76      0.77        75
weighted avg       0.79      0.79      0.78        75


Akurasi fold ini adalah : 78.67  %
Presisi fold ini adalah : 79.17  %
Recall fold ini adalah : 63.33  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[19 11]
 [ 5 40]]

True positive : 19
False negative : 11
False positive : 5
True negative : 40

Classification report : 
               precision    recall  f1-score   support

           1       0.79      0.63      0.70        30
           0       0.78      0.89      0.83        45

    accuracy                           0.79        75
   macro avg       0.79      0.76      0.77        75
weighted avg       0.79      0.79      0.78        75


Akurasi fold ini adalah : 78.67  %
Presisi fold ini adalah : 79.17  %
Recall fold ini adalah : 63.33  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[19 11]
 [ 5 40]]

True positive : 19
False negative : 11
False positive : 5
True negative : 40

Classification report : 
               precision    recall  f1-score   support

           1       0.79      0.63      0.70        30
           0       0.78      0.89      0.83        45

    accuracy                           0.79        75
   macro avg       0.79      0.76      0.77        75
weighted avg       0.79      0.79      0.78        75


Akurasi fold ini adalah : 78.67  %
Presisi fold ini adalah : 79.17  %
Recall fold ini adalah : 63.33  %
===========================================================


nk_acc_c45,nk_acc_adb,adatree,treec45s=k_fold(dnoknn,20,sknar)

>>> FOLD KE  1
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 0.0, 0.0, 0.0, 1, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1, 0.0, 1.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1, 0.0, 1, 0.0, 0.0, 1.0, 0.0, 0.0, 1, 0, 0, 0.0, 1.0, 1.0, 0, 1, 0.0, 1.0, 0.0, 0, 1.0, 0.0, 1, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 1, 0.0, 0.0, 0.0, 0, 0.0]
Confusion matrix : 
 [[13 19]
 [ 9 36]]

True positive : 13
False negative : 19
False positive : 9
True negative : 36

Classification report : 
               precision    recall  f1-score   support

           1       0.59      0.41      0.48        32
           0       0.65      0.80      0.72        45

    accuracy                           0.64        77
   macro avg       0.62      0.60      0.60        77
weighted avg       0.63      0.64      0.62        77


Akurasi fold ini adalah : 63.64  %
Presisi fold ini adalah : 59.09  %
Recall fold ini adalah : 40.62  %
===========================================================
w0 {'Glucose': {127: 0.0, 128: 1.0}} 1.0889875752939042
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.5025458422691002
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5190441987327198
w3 {'Age': {28: 0.0, 29: 1.0}} 0.5417481684658161
w4 {'DiabetesPedigreeFunction': {0.527: 0.0, 1.5270000000000001: 1.0}} 0.426206634910509
w5 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.3320514087499889
w6 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.11892145284649915
w7 {'Glucose': {127: 0.0, 128: 0.0}} 0.15301554206415063
w8 {'Glucose': {127: 1.0, 128: 0.0}} 0.1487266100095366
w9 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.08488968997826894
w10 {'DiabetesPedigreeFunction': {0.527: 0.0, 1.5270000000000001: 0.0}} 0.06635841408909218
w11 {'DiabetesPedigreeFunction': {0.527: 1.0, 1.5270000000000001: 0.0}} 0.08323090265374652
w12 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.08948616027058726
w13 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.04931581023050052
w14 {'Pregnancies': {6: 0.0, 7: 0.0}} 0.029028726606153136
w15 {'Pregnancies': {6: 1.0, 7: 0.0}} 0.025737113094545957
w16 {'Insulin': {120: 0.0, 121: 1.0}} 0.03229422147239687
w17 {'DiabetesPedigreeFunction': {0.527: 1.0, 1.5270000000000001: 0.0}} 0.016588973158100033
w18 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.009522557731094212
w19 {'Age': {28: 0.0, 29: 1.0}} 0.013991974547398135
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[16 16]
 [12 33]]

True positive : 16
False negative : 16
False positive : 12
True negative : 33

Classification report : 
               precision    recall  f1-score   support

           1       0.57      0.50      0.53        32
           0       0.67      0.73      0.70        45

    accuracy                           0.64        77
   macro avg       0.62      0.62      0.62        77
weighted avg       0.63      0.64      0.63        77


Akurasi fold ini adalah : 63.64  %
Presisi fold ini adalah : 57.14  %
Recall fold ini adalah : 50.0  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[18 14]
 [ 8 37]]

True positive : 18
False negative : 14
False positive : 8
True negative : 37

Classification report : 
               precision    recall  f1-score   support

           1       0.69      0.56      0.62        32
           0       0.73      0.82      0.77        45

    accuracy                           0.71        77
   macro avg       0.71      0.69      0.70        77
weighted avg       0.71      0.71      0.71        77


Akurasi fold ini adalah : 71.43  %
Presisi fold ini adalah : 69.23  %
Recall fold ini adalah : 56.25  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[17 15]
 [ 7 38]]

True positive : 17
False negative : 15
False positive : 7
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.71      0.53      0.61        32
           0       0.72      0.84      0.78        45

    accuracy                           0.71        77
   macro avg       0.71      0.69      0.69        77
weighted avg       0.71      0.71      0.71        77


Akurasi fold ini adalah : 71.43  %
Presisi fold ini adalah : 70.83  %
Recall fold ini adalah : 53.12  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0]
Confusion matrix : 
 [[17 15]
 [ 7 38]]

True positive : 17
False negative : 15
False positive : 7
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.71      0.53      0.61        32
           0       0.72      0.84      0.78        45

    accuracy                           0.71        77
   macro avg       0.71      0.69      0.69        77
weighted avg       0.71      0.71      0.71        77


Akurasi fold ini adalah : 71.43  %
Presisi fold ini adalah : 70.83  %
Recall fold ini adalah : 53.12  %
===========================================================
>>> FOLD KE  2
Panjang data test : 77
Panjang data train : 691
Data Test :
 [0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [0.0, 0.0, 0, 0, 0, 1.0, 0, 1.0, 0.0, 0.0, 0.0, 1.0, 0, 0, 1, 0.0, 0.0, 0, 1.0, 0.0, 0, 0.0, 0, 1.0, 1.0, 0, 0, 0.0, 0.0, 0, 1.0, 0.0, 0.0, 1.0, 1, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1, 0.0, 1, 0.0, 1.0, 0, 1, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0, 1.0, 0.0, 1.0, 1.0]
Confusion matrix : 
 [[12 10]
 [12 43]]

True positive : 12
False negative : 10
False positive : 12
True negative : 43

Classification report : 
               precision    recall  f1-score   support

           1       0.50      0.55      0.52        22
           0       0.81      0.78      0.80        55

    accuracy                           0.71        77
   macro avg       0.66      0.66      0.66        77
weighted avg       0.72      0.71      0.72        77


Akurasi fold ini adalah : 71.43  %
Presisi fold ini adalah : 50.0  %
Recall fold ini adalah : 54.55  %
===========================================================
w0 {'Glucose': {128: 1.0, 127: 0.0}} 1.0209952947370655
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.4443497343897111
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5317805593781872
w3 {'Age': {29: 1.0, 28: 0.0}} 0.5279128139420783
w4 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.4146230150594495
w5 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.3671718197505588
w6 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.13217671357044677
w7 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.17142766848161495
w8 {'Glucose': {128: 0.0, 127: 1.0}} 0.14518655468172675
w9 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.12478187451192112
w10 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.08046556132813219
w11 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.0742041557322578
w12 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.06431296384845683
w13 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.0870846447780481
w14 {'Pregnancies': {6: 0.0, 7: 0.0}} 0.027170114652399855
w15 {'Pregnancies': {6: 1.0, 7: 0.0}} 0.03170591108979495
w16 {'Insulin': {120: 0.0, 121: 1.0}} 0.03802724951506011
w17 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.031341729182533494
w18 {'Age': {29: 1.0, 28: 0.0}} 0.02135179914348787
w19 {'Pregnancies': {6: 0.0, 7: 0.0}} 0.011823277593309348
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1]
Confusion matrix : 
 [[15  7]
 [11 44]]

True positive : 15
False negative : 7
False positive : 11
True negative : 44

Classification report : 
               precision    recall  f1-score   support

           1       0.58      0.68      0.62        22
           0       0.86      0.80      0.83        55

    accuracy                           0.77        77
   macro avg       0.72      0.74      0.73        77
weighted avg       0.78      0.77      0.77        77


Akurasi fold ini adalah : 76.62  %
Presisi fold ini adalah : 57.69  %
Recall fold ini adalah : 68.18  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1]
Confusion matrix : 
 [[11 11]
 [ 6 49]]

True positive : 11
False negative : 11
False positive : 6
True negative : 49

Classification report : 
               precision    recall  f1-score   support

           1       0.65      0.50      0.56        22
           0       0.82      0.89      0.85        55

    accuracy                           0.78        77
   macro avg       0.73      0.70      0.71        77
weighted avg       0.77      0.78      0.77        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 64.71  %
Recall fold ini adalah : 50.0  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1]
Confusion matrix : 
 [[11 11]
 [ 6 49]]

True positive : 11
False negative : 11
False positive : 6
True negative : 49

Classification report : 
               precision    recall  f1-score   support

           1       0.65      0.50      0.56        22
           0       0.82      0.89      0.85        55

    accuracy                           0.78        77
   macro avg       0.73      0.70      0.71        77
weighted avg       0.77      0.78      0.77        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 64.71  %
Recall fold ini adalah : 50.0  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1]
Confusion matrix : 
 [[11 11]
 [ 6 49]]

True positive : 11
False negative : 11
False positive : 6
True negative : 49

Classification report : 
               precision    recall  f1-score   support

           1       0.65      0.50      0.56        22
           0       0.82      0.89      0.85        55

    accuracy                           0.78        77
   macro avg       0.73      0.70      0.71        77
weighted avg       0.77      0.78      0.77        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 64.71  %
Recall fold ini adalah : 50.0  %
===========================================================
>>> FOLD KE  3
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 1.0, 0, 0, 0.0, 1.0, 1.0, 0.0, 0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0, 0, 0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0, 0.0, 1, 1.0, 0.0, 1.0, 0, 0, 1.0, 1.0, 0.0, 0.0, 0.0, 0, 1.0, 0, 1, 1.0, 0.0, 1.0, 0, 1.0, 1.0, 0.0, 0.0, 1.0, 0, 1.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0]
Confusion matrix : 
 [[26  8]
 [12 31]]

True positive : 26
False negative : 8
False positive : 12
True negative : 31

Classification report : 
               precision    recall  f1-score   support

           1       0.68      0.76      0.72        34
           0       0.79      0.72      0.76        43

    accuracy                           0.74        77
   macro avg       0.74      0.74      0.74        77
weighted avg       0.75      0.74      0.74        77


Akurasi fold ini adalah : 74.03  %
Presisi fold ini adalah : 68.42  %
Recall fold ini adalah : 76.47  %
===========================================================
w0 {'Glucose': {128: 1.0, 127: 0.0}} 1.0061808442091453
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.5024591601430485
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5458944349971626
w3 {'Age': {29: 1.0, 28: 0.0}} 0.5695704704928805
w4 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.4214000652249165
w5 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.2130276371438679
w6 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.20335518906239436
w7 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.26838600795710177
w8 {'Glucose': {128: 0.0, 127: 1.0}} 0.13765369097981725
w9 {'Pregnancies': {6: 1.0, 7: 0.0}} 0.061953519844992005
w10 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.11929047856460782
w11 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.08374453782811403
w12 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.026344139933802586
w13 {'Pregnancies': {6: 0.0, 7: 0.0}} 0.031974556840658475
w14 {'Pregnancies': {6: 1.0, 7: 0.0}} 0.030207621085582063
w15 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.014794620728060641
w16 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.01677426136374761
w17 {'SkinThickness': {32: 0.0, 31: 1.0}} 0.02045416069622986
w18 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 0.0}} 0.017639517244763005
w19 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.01976314962763356
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1]
Confusion matrix : 
 [[27  7]
 [11 32]]

True positive : 27
False negative : 7
False positive : 11
True negative : 32

Classification report : 
               precision    recall  f1-score   support

           1       0.71      0.79      0.75        34
           0       0.82      0.74      0.78        43

    accuracy                           0.77        77
   macro avg       0.77      0.77      0.77        77
weighted avg       0.77      0.77      0.77        77


Akurasi fold ini adalah : 76.62  %
Presisi fold ini adalah : 71.05  %
Recall fold ini adalah : 79.41  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1]
Confusion matrix : 
 [[23 11]
 [ 5 38]]

True positive : 23
False negative : 11
False positive : 5
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.82      0.68      0.74        34
           0       0.78      0.88      0.83        43

    accuracy                           0.79        77
   macro avg       0.80      0.78      0.78        77
weighted avg       0.80      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 82.14  %
Recall fold ini adalah : 67.65  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1]
Confusion matrix : 
 [[23 11]
 [ 5 38]]

True positive : 23
False negative : 11
False positive : 5
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.82      0.68      0.74        34
           0       0.78      0.88      0.83        43

    accuracy                           0.79        77
   macro avg       0.80      0.78      0.78        77
weighted avg       0.80      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 82.14  %
Recall fold ini adalah : 67.65  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1]
Confusion matrix : 
 [[23 11]
 [ 5 38]]

True positive : 23
False negative : 11
False positive : 5
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.82      0.68      0.74        34
           0       0.78      0.88      0.83        43

    accuracy                           0.79        77
   macro avg       0.80      0.78      0.78        77
weighted avg       0.80      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 82.14  %
Recall fold ini adalah : 67.65  %
===========================================================
>>> FOLD KE  4
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 0.0, 0.0, 0.0, 0.0, 1, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0.0, 0, 0.0, 0, 0.0, 0.0, 0.0, 0, 1, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1, 0, 0.0, 1.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0.0, 1, 1, 1.0, 0.0, 1.0, 1.0, 1.0, 0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 1.0, 0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0, 0]
Confusion matrix : 
 [[15 15]
 [14 33]]

True positive : 15
False negative : 15
False positive : 14
True negative : 33

Classification report : 
               precision    recall  f1-score   support

           1       0.52      0.50      0.51        30
           0       0.69      0.70      0.69        47

    accuracy                           0.62        77
   macro avg       0.60      0.60      0.60        77
weighted avg       0.62      0.62      0.62        77


Akurasi fold ini adalah : 62.34  %
Presisi fold ini adalah : 51.72  %
Recall fold ini adalah : 50.0  %
===========================================================
w0 {'Glucose': {128: 1.0, 127: 0.0}} 1.0660735328813402
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.4673591524883059
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5986289856457404
w3 {'Age': {29: 1.0, 28: 0.0}} 0.560385160090479
w4 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.46902810782250776
w5 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.2079901522028697
w6 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.2945982213619648
w7 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.25109746832551766
w8 {'Glucose': {128: 0.0, 127: 1.0}} 0.18427836366849215
w9 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.11411890226866249
w10 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.1301425204715043
w11 {'Insulin': {120: 0.0, 121: 1.0}} 0.029896108432542205
w12 {'Age': {29: 1.0, 28: 0.0}} 0.039943769899428445
w13 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.02696598575730403
w14 {'Insulin': {120: 1.0, 121: 1.0}} 0.028974185664093842
w15 {'Insulin': {120: 0.0, 121: 1.0}} 0.014672964631899488
w16 {'BMI': {28.8: 0.0, 27.8: 1.0}} 0.00919335448427747
w17 {'Glucose': {128: 0.0, 127: 1.0}} 0.014862142141176477
w18 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 1.0}} 0.010488238430331783
w19 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.01185926611851229
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[17 13]
 [13 34]]

True positive : 17
False negative : 13
False positive : 13
True negative : 34

Classification report : 
               precision    recall  f1-score   support

           1       0.57      0.57      0.57        30
           0       0.72      0.72      0.72        47

    accuracy                           0.66        77
   macro avg       0.65      0.65      0.65        77
weighted avg       0.66      0.66      0.66        77


Akurasi fold ini adalah : 66.23  %
Presisi fold ini adalah : 56.67  %
Recall fold ini adalah : 56.67  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[14 16]
 [ 9 38]]

True positive : 14
False negative : 16
False positive : 9
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.61      0.47      0.53        30
           0       0.70      0.81      0.75        47

    accuracy                           0.68        77
   macro avg       0.66      0.64      0.64        77
weighted avg       0.67      0.68      0.67        77


Akurasi fold ini adalah : 67.53  %
Presisi fold ini adalah : 60.87  %
Recall fold ini adalah : 46.67  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[14 16]
 [ 9 38]]

True positive : 14
False negative : 16
False positive : 9
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.61      0.47      0.53        30
           0       0.70      0.81      0.75        47

    accuracy                           0.68        77
   macro avg       0.66      0.64      0.64        77
weighted avg       0.67      0.68      0.67        77


Akurasi fold ini adalah : 67.53  %
Presisi fold ini adalah : 60.87  %
Recall fold ini adalah : 46.67  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[14 16]
 [ 9 38]]

True positive : 14
False negative : 16
False positive : 9
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.61      0.47      0.53        30
           0       0.70      0.81      0.75        47

    accuracy                           0.68        77
   macro avg       0.66      0.64      0.64        77
weighted avg       0.67      0.68      0.67        77


Akurasi fold ini adalah : 67.53  %
Presisi fold ini adalah : 60.87  %
Recall fold ini adalah : 46.67  %
===========================================================
>>> FOLD KE  5
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1.0, 1.0, 0, 0, 0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0, 0.0, 0.0, 0, 0.0, 0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0, 1.0, 0.0, 0.0, 1.0, 1.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0, 0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0, 0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0, 0.0, 1.0, 0.0, 0.0, 0, 0.0, 0.0, 0]
Confusion matrix : 
 [[16 11]
 [ 7 43]]

True positive : 16
False negative : 11
False positive : 7
True negative : 43

Classification report : 
               precision    recall  f1-score   support

           1       0.70      0.59      0.64        27
           0       0.80      0.86      0.83        50

    accuracy                           0.77        77
   macro avg       0.75      0.73      0.73        77
weighted avg       0.76      0.77      0.76        77


Akurasi fold ini adalah : 76.62  %
Presisi fold ini adalah : 69.57  %
Recall fold ini adalah : 59.26  %
===========================================================
w0 {'Glucose': {128: 1.0, 127: 0.0}} 1.013575245982202
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.47643958671141695
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5609508265675935
w3 {'Age': {29: 1.0, 28: 0.0}} 0.5379406226090246
w4 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 0.0}} 0.42895937150697955
w5 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.2909040344487451
w6 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.21635093576834402
w7 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.3044435349211881
w8 {'Glucose': {128: 0.0, 127: 1.0}} 0.1542386560887124
w9 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.1259728325696127
w10 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.1135573286672109
w11 {'SkinThickness': {32: 1.0, 31: 1.0}} 0.05529579754715737
w12 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.06416438693030486
w13 {'Insulin': {120: 1.0, 121: 1.0}} 0.023558246853703415
w14 {'Insulin': {120: 0.0, 121: 1.0}} 0.029846572869663718
w15 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.02014109367121675
w16 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.033462294652272476
w17 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.02208120763264851
w18 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.017142479498485876
w19 {'Pregnancies': {6: 1.0, 7: 1.0}} 0.010949551963433196
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[16 11]
 [ 7 43]]

True positive : 16
False negative : 11
False positive : 7
True negative : 43

Classification report : 
               precision    recall  f1-score   support

           1       0.70      0.59      0.64        27
           0       0.80      0.86      0.83        50

    accuracy                           0.77        77
   macro avg       0.75      0.73      0.73        77
weighted avg       0.76      0.77      0.76        77


Akurasi fold ini adalah : 76.62  %
Presisi fold ini adalah : 69.57  %
Recall fold ini adalah : 59.26  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[16 11]
 [ 5 45]]

True positive : 16
False negative : 11
False positive : 5
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.76      0.59      0.67        27
           0       0.80      0.90      0.85        50

    accuracy                           0.79        77
   macro avg       0.78      0.75      0.76        77
weighted avg       0.79      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 76.19  %
Recall fold ini adalah : 59.26  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[16 11]
 [ 5 45]]

True positive : 16
False negative : 11
False positive : 5
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.76      0.59      0.67        27
           0       0.80      0.90      0.85        50

    accuracy                           0.79        77
   macro avg       0.78      0.75      0.76        77
weighted avg       0.79      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 76.19  %
Recall fold ini adalah : 59.26  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[16 11]
 [ 5 45]]

True positive : 16
False negative : 11
False positive : 5
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.76      0.59      0.67        27
           0       0.80      0.90      0.85        50

    accuracy                           0.79        77
   macro avg       0.78      0.75      0.76        77
weighted avg       0.79      0.79      0.79        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 76.19  %
Recall fold ini adalah : 59.26  %
===========================================================
>>> FOLD KE  6
Panjang data test : 77
Panjang data train : 691
Data Test :
 [0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0, 0.0, 1.0, 0.0, 0, 1.0, 0, 0.0, 0.0, 1.0, 0, 0.0, 1.0, 0.0, 0.0, 0, 0, 1.0, 0, 0, 1.0, 0.0, 1.0, 1.0, 0.0, 1, 0.0, 1.0, 0, 0.0, 0.0, 0.0, 1.0, 1.0, 0, 1.0, 1.0, 0.0, 0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0, 0.0, 0.0, 0.0, 0, 1.0, 0.0, 0.0, 0.0, 1, 1, 0.0, 1.0, 1, 0.0, 0]
Confusion matrix : 
 [[20 10]
 [ 9 38]]

True positive : 20
False negative : 10
False positive : 9
True negative : 38

Classification report : 
               precision    recall  f1-score   support

           1       0.69      0.67      0.68        30
           0       0.79      0.81      0.80        47

    accuracy                           0.75        77
   macro avg       0.74      0.74      0.74        77
weighted avg       0.75      0.75      0.75        77


Akurasi fold ini adalah : 75.32  %
Presisi fold ini adalah : 68.97  %
Recall fold ini adalah : 66.67  %
===========================================================
w0 {'Glucose': {128: 1.0, 127: 0.0}} 1.0061808442091453
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.4783997974499721
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.49263919847481247
w3 {'Age': {29: 1.0, 28: 0.0}} 0.5185618264045502
w4 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.4269708013271865
w5 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.3799576850840064
w6 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.1026153271886141
w7 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.1589440290287949
w8 {'Glucose': {128: 0.0, 127: 1.0}} 0.17412299009443705
w9 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.12792405781993893
w10 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 0.0}} 0.06901787357181162
w11 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.08226841988681756
w12 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.06452515847587553
w13 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.04086844030198589
w14 {'Insulin': {120: 0.0, 121: 1.0}} 0.04929115243699896
w15 {'SkinThickness': {32: 0.0, 31: 1.0}} 0.03597217091512214
w16 {'Pregnancies': {6: 0.0, 7: 0.0}} 0.020791996692471004
w17 {'Pregnancies': {6: 1.0, 7: 0.0}} 0.029286287382713893
w18 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.014007415207947554
w19 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.018524349452760144
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0]
Confusion matrix : 
 [[21  9]
 [ 8 39]]

True positive : 21
False negative : 9
False positive : 8
True negative : 39

Classification report : 
               precision    recall  f1-score   support

           1       0.72      0.70      0.71        30
           0       0.81      0.83      0.82        47

    accuracy                           0.78        77
   macro avg       0.77      0.76      0.77        77
weighted avg       0.78      0.78      0.78        77


Akurasi fold ini adalah : 77.92  %
Presisi fold ini adalah : 72.41  %
Recall fold ini adalah : 70.0  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 0]
Confusion matrix : 
 [[18 12]
 [ 4 43]]

True positive : 18
False negative : 12
False positive : 4
True negative : 43

Classification report : 
               precision    recall  f1-score   support

           1       0.82      0.60      0.69        30
           0       0.78      0.91      0.84        47

    accuracy                           0.79        77
   macro avg       0.80      0.76      0.77        77
weighted avg       0.80      0.79      0.78        77


Akurasi fold ini adalah : 79.22  %
Presisi fold ini adalah : 81.82  %
Recall fold ini adalah : 60.0  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0]
Confusion matrix : 
 [[18 12]
 [ 3 44]]

True positive : 18
False negative : 12
False positive : 3
True negative : 44

Classification report : 
               precision    recall  f1-score   support

           1       0.86      0.60      0.71        30
           0       0.79      0.94      0.85        47

    accuracy                           0.81        77
   macro avg       0.82      0.77      0.78        77
weighted avg       0.81      0.81      0.80        77


Akurasi fold ini adalah : 80.52  %
Presisi fold ini adalah : 85.71  %
Recall fold ini adalah : 60.0  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[18 12]
 [ 2 45]]

True positive : 18
False negative : 12
False positive : 2
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.90      0.60      0.72        30
           0       0.79      0.96      0.87        47

    accuracy                           0.82        77
   macro avg       0.84      0.78      0.79        77
weighted avg       0.83      0.82      0.81        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 90.0  %
Recall fold ini adalah : 60.0  %
===========================================================
>>> FOLD KE  7
Panjang data test : 77
Panjang data train : 691
Data Test :
 [0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1, 0.0, 0.0, 0, 0, 1.0, 1.0, 1.0, 1, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0, 1.0, 1.0, 0.0, 0, 0, 1.0, 1.0, 1, 1.0, 0, 1.0, 0.0, 0.0, 1.0, 1.0, 0, 0.0, 0.0, 0.0, 0, 1.0, 0, 0.0, 0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0, 0.0, 0, 0, 0.0, 1.0, 0.0, 1.0, 1, 0, 0, 0, 1.0, 0.0, 0, 0, 0, 0.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0]
Confusion matrix : 
 [[ 9  5]
 [16 47]]

True positive : 9
False negative : 5
False positive : 16
True negative : 47

Classification report : 
               precision    recall  f1-score   support

           1       0.36      0.64      0.46        14
           0       0.90      0.75      0.82        63

    accuracy                           0.73        77
   macro avg       0.63      0.69      0.64        77
weighted avg       0.80      0.73      0.75        77


Akurasi fold ini adalah : 72.73  %
Presisi fold ini adalah : 36.0  %
Recall fold ini adalah : 64.29  %
===========================================================
w0 {'Glucose': {128: 1.0, 127: 0.0}} 1.013575245982202
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.3864602230214747
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5248551091408121
w3 {'Age': {29: 1.0, 28: 0.0}} 0.5357816358981357
w4 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.4502685505967478
w5 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.34449174353563367
w6 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.11141775781781224
w7 {'Glucose': {128: 0.0, 127: 0.0}} 0.16130843812645906
w8 {'Glucose': {128: 0.0, 127: 1.0}} 0.1604827588631298
w9 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.08912788485780045
w10 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.1284319647235065
w11 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.07986046253648521
w12 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.08535240941321488
w13 {'Pregnancies': {6: 0.0, 7: 0.0}} 0.02415129371392441
w14 {'Pregnancies': {6: 1.0, 7: 0.0}} 0.03026539685326428
w15 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.01514770443041451
w16 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.023205914529791524
w17 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 0.0}} 0.013628895491653758
w18 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.016848329981988503
w19 {'Age': {29: 1.0, 28: 0.0}} 0.012223853986120949
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0]
Confusion matrix : 
 [[12  2]
 [16 47]]

True positive : 12
False negative : 2
False positive : 16
True negative : 47

Classification report : 
               precision    recall  f1-score   support

           1       0.43      0.86      0.57        14
           0       0.96      0.75      0.84        63

    accuracy                           0.77        77
   macro avg       0.69      0.80      0.71        77
weighted avg       0.86      0.77      0.79        77


Akurasi fold ini adalah : 76.62  %
Presisi fold ini adalah : 42.86  %
Recall fold ini adalah : 85.71  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[ 6  8]
 [12 51]]

True positive : 6
False negative : 8
False positive : 12
True negative : 51

Classification report : 
               precision    recall  f1-score   support

           1       0.33      0.43      0.38        14
           0       0.86      0.81      0.84        63

    accuracy                           0.74        77
   macro avg       0.60      0.62      0.61        77
weighted avg       0.77      0.74      0.75        77


Akurasi fold ini adalah : 74.03  %
Presisi fold ini adalah : 33.33  %
Recall fold ini adalah : 42.86  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[ 7  7]
 [12 51]]

True positive : 7
False negative : 7
False positive : 12
True negative : 51

Classification report : 
               precision    recall  f1-score   support

           1       0.37      0.50      0.42        14
           0       0.88      0.81      0.84        63

    accuracy                           0.75        77
   macro avg       0.62      0.65      0.63        77
weighted avg       0.79      0.75      0.77        77


Akurasi fold ini adalah : 75.32  %
Presisi fold ini adalah : 36.84  %
Recall fold ini adalah : 50.0  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[ 7  7]
 [12 51]]

True positive : 7
False negative : 7
False positive : 12
True negative : 51

Classification report : 
               precision    recall  f1-score   support

           1       0.37      0.50      0.42        14
           0       0.88      0.81      0.84        63

    accuracy                           0.75        77
   macro avg       0.62      0.65      0.63        77
weighted avg       0.79      0.75      0.77        77


Akurasi fold ini adalah : 75.32  %
Presisi fold ini adalah : 36.84  %
Recall fold ini adalah : 50.0  %
===========================================================
>>> FOLD KE  8
Panjang data test : 77
Panjang data train : 691
Data Test :
 [1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [0.0, 1, 0.0, 1.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0, 1.0, 0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1, 0.0, 0.0, 1.0, 1.0, 0, 0.0, 0.0, 1, 0, 1, 0.0, 1.0, 0, 1.0, 0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0, 0, 0, 0, 1, 1.0, 0.0, 1.0, 0, 1.0, 0, 0, 0.0, 1.0, 0.0, 1, 0]
Confusion matrix : 
 [[18  7]
 [ 8 44]]

True positive : 18
False negative : 7
False positive : 8
True negative : 44

Classification report : 
               precision    recall  f1-score   support

           1       0.69      0.72      0.71        25
           0       0.86      0.85      0.85        52

    accuracy                           0.81        77
   macro avg       0.78      0.78      0.78        77
weighted avg       0.81      0.81      0.81        77


Akurasi fold ini adalah : 80.52  %
Presisi fold ini adalah : 69.23  %
Recall fold ini adalah : 72.0  %
===========================================================
w0 {'Glucose': {128: 1.0, 127: 0.0}} 0.9841482072697947
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.46224846486154436
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5196436942724918
w3 {'Age': {29: 1.0, 28: 0.0}} 0.5303185432481096
w4 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 0.0}} 0.39130748997846165
w5 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.23845659656952886
w6 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.16877180084536242
w7 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.2467368055831519
w8 {'Glucose': {128: 0.0, 127: 1.0}} 0.15487011239127857
w9 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.09261000885578945
w10 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.08674364962054859
w11 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.06227603172806396
w12 {'Age': {29: 1.0, 28: 1.0}} 0.07694463918883722
w13 {'Age': {29: 1.0, 28: 0.0}} 0.031292415780461734
w14 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.03365186221395913
w15 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.022289551716320497
w16 {'Pregnancies': {6: 1.0, 7: 1.0}} 0.010996047299479447
w17 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.01582432864914028
w18 {'Insulin': {120: 0.0, 121: 1.0}} 0.008194564468002383
w19 {'BMI': {28.8: 1.0, 27.8: 1.0}} 0.011459215961354628
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0]
Confusion matrix : 
 [[18  7]
 [ 7 45]]

True positive : 18
False negative : 7
False positive : 7
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.72      0.72      0.72        25
           0       0.87      0.87      0.87        52

    accuracy                           0.82        77
   macro avg       0.79      0.79      0.79        77
weighted avg       0.82      0.82      0.82        77


Akurasi fold ini adalah : 81.82  %
Presisi fold ini adalah : 72.0  %
Recall fold ini adalah : 72.0  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0]
Confusion matrix : 
 [[20  5]
 [ 4 48]]

True positive : 20
False negative : 5
False positive : 4
True negative : 48

Classification report : 
               precision    recall  f1-score   support

           1       0.83      0.80      0.82        25
           0       0.91      0.92      0.91        52

    accuracy                           0.88        77
   macro avg       0.87      0.86      0.87        77
weighted avg       0.88      0.88      0.88        77


Akurasi fold ini adalah : 88.31  %
Presisi fold ini adalah : 83.33  %
Recall fold ini adalah : 80.0  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0]
Confusion matrix : 
 [[20  5]
 [ 4 48]]

True positive : 20
False negative : 5
False positive : 4
True negative : 48

Classification report : 
               precision    recall  f1-score   support

           1       0.83      0.80      0.82        25
           0       0.91      0.92      0.91        52

    accuracy                           0.88        77
   macro avg       0.87      0.86      0.87        77
weighted avg       0.88      0.88      0.88        77


Akurasi fold ini adalah : 88.31  %
Presisi fold ini adalah : 83.33  %
Recall fold ini adalah : 80.0  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 1, 0, 1, 0]
Confusion matrix : 
 [[20  5]
 [ 4 48]]

True positive : 20
False negative : 5
False positive : 4
True negative : 48

Classification report : 
               precision    recall  f1-score   support

           1       0.83      0.80      0.82        25
           0       0.91      0.92      0.91        52

    accuracy                           0.88        77
   macro avg       0.87      0.86      0.87        77
weighted avg       0.88      0.88      0.88        77


Akurasi fold ini adalah : 88.31  %
Presisi fold ini adalah : 83.33  %
Recall fold ini adalah : 80.0  %
===========================================================
>>> FOLD KE  9
Panjang data test : 77
Panjang data train : 691
Data Test :
 [0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [0.0, 0, 1.0, 0.0, 0, 0, 1.0, 0.0, 0.0, 0.0, 0, 1.0, 1.0, 0, 0, 0.0, 0, 0, 0.0, 0.0, 0.0, 0.0, 1.0, 0, 0.0, 1.0, 1.0, 0.0, 0, 1.0, 1.0, 1.0, 1.0, 0, 0, 0.0, 0.0, 0, 0, 0.0, 0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 0.0, 0, 1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0, 1.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0, 1.0, 0.0, 1.0, 0]
Confusion matrix : 
 [[14 10]
 [11 42]]

True positive : 14
False negative : 10
False positive : 11
True negative : 42

Classification report : 
               precision    recall  f1-score   support

           1       0.56      0.58      0.57        24
           0       0.81      0.79      0.80        53

    accuracy                           0.73        77
   macro avg       0.68      0.69      0.69        77
weighted avg       0.73      0.73      0.73        77


Akurasi fold ini adalah : 72.73  %
Presisi fold ini adalah : 56.0  %
Recall fold ini adalah : 58.33  %
===========================================================
w0 {'Glucose': {128: 1.0, 127: 0.0}} 1.0434127393134984
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.44777837761030315
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.552772162274304
w3 {'Age': {29: 1.0, 28: 0.0}} 0.543792111842528
w4 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.43442504669735427
w5 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.3176231314373438
w6 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.16713539242381212
w7 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.23893724973283714
w8 {'Glucose': {128: 0.0, 127: 1.0}} 0.16360946683035843
w9 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.07148949121830761
w10 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.07333381810420848
w11 {'Insulin': {120: 0.0, 121: 1.0}} 0.09362085805671688
w12 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.05579386210207226
w13 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.04762790249963228
w14 {'Age': {29: 1.0, 28: 0.0}} 0.04902867645740026
w15 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 1.0}} 0.03789745477763916
w16 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.014042660671587062
w17 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.014576774546838424
w18 {'Insulin': {120: 1.0, 121: 0.0}} 0.015159819443421865
w19 {'Glucose': {128: 0.0, 127: 0.0}} 0.01510706297110345
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[16  8]
 [13 40]]

True positive : 16
False negative : 8
False positive : 13
True negative : 40

Classification report : 
               precision    recall  f1-score   support

           1       0.55      0.67      0.60        24
           0       0.83      0.75      0.79        53

    accuracy                           0.73        77
   macro avg       0.69      0.71      0.70        77
weighted avg       0.75      0.73      0.73        77


Akurasi fold ini adalah : 72.73  %
Presisi fold ini adalah : 55.17  %
Recall fold ini adalah : 66.67  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[11 13]
 [ 8 45]]

True positive : 11
False negative : 13
False positive : 8
True negative : 45

Classification report : 
               precision    recall  f1-score   support

           1       0.58      0.46      0.51        24
           0       0.78      0.85      0.81        53

    accuracy                           0.73        77
   macro avg       0.68      0.65      0.66        77
weighted avg       0.71      0.73      0.72        77


Akurasi fold ini adalah : 72.73  %
Presisi fold ini adalah : 57.89  %
Recall fold ini adalah : 45.83  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[11 13]
 [ 7 46]]

True positive : 11
False negative : 13
False positive : 7
True negative : 46

Classification report : 
               precision    recall  f1-score   support

           1       0.61      0.46      0.52        24
           0       0.78      0.87      0.82        53

    accuracy                           0.74        77
   macro avg       0.70      0.66      0.67        77
weighted avg       0.73      0.74      0.73        77


Akurasi fold ini adalah : 74.03  %
Presisi fold ini adalah : 61.11  %
Recall fold ini adalah : 45.83  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0]
Confusion matrix : 
 [[11 13]
 [ 7 46]]

True positive : 11
False negative : 13
False positive : 7
True negative : 46

Classification report : 
               precision    recall  f1-score   support

           1       0.61      0.46      0.52        24
           0       0.78      0.87      0.82        53

    accuracy                           0.74        77
   macro avg       0.70      0.66      0.67        77
weighted avg       0.73      0.74      0.73        77


Akurasi fold ini adalah : 74.03  %
Presisi fold ini adalah : 61.11  %
Recall fold ini adalah : 45.83  %
===========================================================
>>> FOLD KE  10
Panjang data test : 75
Panjang data train : 693
Data Test :
 [1, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0]
*******************
Algoritma C4.5
*******************
Hasil Prediksi C45 :
 [1, 0, 0.0, 1.0, 0, 0, 0.0, 0, 0, 1.0, 1.0, 0.0, 0.0, 0.0, 0, 1.0, 1.0, 1.0, 0.0, 1, 0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0, 0.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0, 0, 0.0, 0, 1.0, 0, 0.0, 1, 0.0, 0.0, 1.0, 1, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0, 0.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
Confusion matrix : 
 [[19 11]
 [ 6 39]]

True positive : 19
False negative : 11
False positive : 6
True negative : 39

Classification report : 
               precision    recall  f1-score   support

           1       0.76      0.63      0.69        30
           0       0.78      0.87      0.82        45

    accuracy                           0.77        75
   macro avg       0.77      0.75      0.76        75
weighted avg       0.77      0.77      0.77        75


Akurasi fold ini adalah : 77.33  %
Presisi fold ini adalah : 76.0  %
Recall fold ini adalah : 63.33  %
===========================================================
w0 {'Glucose': {128: 1.0, 127: 0.0}} 0.9954280524328791
w1 {'BMI': {28.8: 0.0, 27.8: 0.0}} 0.4960962675197953
w2 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.5793515397651382
w3 {'Age': {29: 1.0, 28: 0.0}} 0.5088250032061252
w4 {'DiabetesPedigreeFunction': {1.5270000000000001: 0.0, 0.527: 0.0}} 0.42240442841875286
w5 {'DiabetesPedigreeFunction': {1.5270000000000001: 1.0, 0.527: 0.0}} 0.2941688406433191
w6 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.19359852648565157
w7 {'BMI': {28.8: 1.0, 27.8: 0.0}} 0.27460468148564887
w8 {'Glucose': {128: 0.0, 127: 1.0}} 0.16007636110845413
w9 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.10368776088375188
w10 {'SkinThickness': {32: 1.0, 31: 0.0}} 0.0711609985833574
w11 {'BloodPressure': {69: 1.0, 68: 1.0}} 0.07055308712841289
w12 {'BloodPressure': {69: 0.0, 68: 1.0}} 0.07372795423874279
w13 {'Insulin': {120: 0.0, 121: 1.0}} 0.045615771971840455
w14 {'Pregnancies': {6: 1.0, 7: 1.0}} 0.03807727933399282
w15 {'Pregnancies': {6: 0.0, 7: 1.0}} 0.033227503737155786
w16 {'Insulin': {120: 1.0, 121: 1.0}} 0.01685753181252852
w17 {'Insulin': {120: 0.0, 121: 1.0}} 0.018017076974993405
w18 {'BMI': {28.8: 1.0, 27.8: 1.0}} 0.007765807218662133
w19 {'BMI': {28.8: 0.0, 27.8: 1.0}} 0.010433447997290273
*******************
Adaboost  t- 5
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[21  9]
 [ 5 40]]

True positive : 21
False negative : 9
False positive : 5
True negative : 40

Classification report : 
               precision    recall  f1-score   support

           1       0.81      0.70      0.75        30
           0       0.82      0.89      0.85        45

    accuracy                           0.81        75
   macro avg       0.81      0.79      0.80        75
weighted avg       0.81      0.81      0.81        75


Akurasi fold ini adalah : 81.33  %
Presisi fold ini adalah : 80.77  %
Recall fold ini adalah : 70.0  %
===========================================================
*******************
Adaboost  t- 10
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[19 11]
 [ 4 41]]

True positive : 19
False negative : 11
False positive : 4
True negative : 41

Classification report : 
               precision    recall  f1-score   support

           1       0.83      0.63      0.72        30
           0       0.79      0.91      0.85        45

    accuracy                           0.80        75
   macro avg       0.81      0.77      0.78        75
weighted avg       0.80      0.80      0.79        75


Akurasi fold ini adalah : 80.0  %
Presisi fold ini adalah : 82.61  %
Recall fold ini adalah : 63.33  %
===========================================================
*******************
Adaboost  t- 15
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[19 11]
 [ 4 41]]

True positive : 19
False negative : 11
False positive : 4
True negative : 41

Classification report : 
               precision    recall  f1-score   support

           1       0.83      0.63      0.72        30
           0       0.79      0.91      0.85        45

    accuracy                           0.80        75
   macro avg       0.81      0.77      0.78        75
weighted avg       0.80      0.80      0.79        75


Akurasi fold ini adalah : 80.0  %
Presisi fold ini adalah : 82.61  %
Recall fold ini adalah : 63.33  %
===========================================================
*******************
Adaboost  t- 20
*******************
Hasil Prediksi Adaboost :
 [1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0]
Confusion matrix : 
 [[19 11]
 [ 4 41]]

True positive : 19
False negative : 11
False positive : 4
True negative : 41

Classification report : 
               precision    recall  f1-score   support

           1       0.83      0.63      0.72        30
           0       0.79      0.91      0.85        45

    accuracy                           0.80        75
   macro avg       0.81      0.77      0.78        75
weighted avg       0.80      0.80      0.79        75


Akurasi fold ini adalah : 80.0  %
Presisi fold ini adalah : 82.61  %
Recall fold ini adalah : 63.33  %
===========================================================


def get_colname(df):
    tempp=df.columns
    return tempp[0][4:]
    
def get_max_at_fold(df):
    print("====================================")
    print("Nilai Max keseluruhan fold :")
    print("====================================")
    sr = pd.Series([df.iloc[i].max() for i in df.index],index=[i+1 for i in df.index])
    print(sr)
    print("\n>>> Nilai maksimal keseluruhan fold :")
    print(sr.idxmax(), "=",sr.max())

def get_mean_max_at_fold(df):
    print("====================================")
    print("Nilai rata-rata keseluruhan fold :")
    print("====================================")
    sr = pd.Series([df[i].mean() for i in df.columns],index=[i for i in df.columns])
    print(sr)
    print("\n>>> Nilai maksimal rata-rata keseluruhan fold :")
    print(sr.idxmax(), "=",sr.max())
    
def conclute_to_df(acc_adb, acc_c45):
    k = ["k"+str(i+1) for i in range(10)]
    sax = pd.DataFrame({'k': k})
    for i in acc_c45:
        idc45 = 'C45-'+str(i)
        sax[idc45] = acc_c45[i]
        for zz,skz in enumerate(sknar):
            adin='t-'+str(skz)+' '+str(i)
            sax[adin]=[x[zz] for x in acc_adb[i]]

    # sax.to_csv('noknn9.csv', index=False)
    return sax
    
def get_df(sax,pat):
    temp=[]
    for i in pat:
        col_aku = sax.columns[sax.columns.str.contains(i)].tolist() 
        temp.append(sax.get(col_aku))

    return temp

def get_conclute_data(acc_adb,acc_c45):
    sax = conclute_to_df(acc_adb,acc_c45)
    temp = get_df(sax,acc_adb.keys())
    for i in temp :
        i_name=get_colname(i)
        print("******************")
        print("Nilai",i_name)
        print("******************")
        print(i)
        get_max_at_fold(i)
        get_mean_max_at_fold(i)


get_conclute_data(wk_acc_adb,wk_acc_c45)

******************
Nilai akurasi
******************
   C45-akurasi  t-5 akurasi  t-10 akurasi  t-15 akurasi  t-20 akurasi
0        63.64        64.94         70.13         70.13         70.13
1        74.03        74.03         77.92         77.92         77.92
2        81.82        76.62         77.92         79.22         79.22
3        67.53        66.23         70.13         67.53         70.13
4        77.92        77.92         79.22         79.22         80.52
5        81.82        77.92         81.82         81.82         81.82
6        77.92        79.22         81.82         81.82         81.82
7        81.82        84.42         85.71         85.71         85.71
8        70.13        71.43         72.73         72.73         72.73
9        81.33        81.33         78.67         78.67         78.67
====================================
Nilai Max keseluruhan fold :
====================================
1     70.13
2     77.92
3     81.82
4     70.13
5     80.52
6     81.82
7     81.82
8     85.71
9     72.73
10    81.33
dtype: float64

>>> Nilai maksimal keseluruhan fold :
8 = 85.71
====================================
Nilai rata-rata keseluruhan fold :
====================================
C45-akurasi     75.796
t-5 akurasi     75.406
t-10 akurasi    77.607
t-15 akurasi    77.477
t-20 akurasi    77.867
dtype: float64

>>> Nilai maksimal rata-rata keseluruhan fold :
t-20 akurasi = 77.86699999999999
******************
Nilai presisi
******************
   C45-presisi  t-5 presisi  t-10 presisi  t-15 presisi  t-20 presisi
0        57.69        59.26         63.64         64.52         64.52
1        53.57        53.12         61.90         61.90         60.87
2        76.32        75.00         74.29         75.00         75.00
3        57.58        56.67         62.07         57.58         62.07
4        70.83        75.00         72.00         76.19         75.00
5        76.67        74.07         80.77         80.77         78.57
6        44.00        43.75         50.00         50.00         50.00
7        66.67        68.57         75.00         75.00         75.00
8        51.85        53.12         56.00         56.52         56.52
9        78.57        78.57         79.17         79.17         79.17
====================================
Nilai Max keseluruhan fold :
====================================
1     64.52
2     61.90
3     76.32
4     62.07
5     76.19
6     80.77
7     50.00
8     75.00
9     56.52
10    79.17
dtype: float64

>>> Nilai maksimal keseluruhan fold :
6 = 80.77
====================================
Nilai rata-rata keseluruhan fold :
====================================
C45-presisi     63.375
t-5 presisi     63.713
t-10 presisi    67.484
t-15 presisi    67.665
t-20 presisi    67.672
dtype: float64

>>> Nilai maksimal rata-rata keseluruhan fold :
t-20 presisi = 67.672
******************
Nilai recall
******************
   C45-recall  t-5 recall  t-10 recall  t-15 recall  t-20 recall
0       46.88       50.00        65.62        62.50        62.50
1       68.18       77.27        59.09        59.09        63.64
2       85.29       70.59        76.47        79.41        79.41
3       63.33       56.67        60.00        63.33        60.00
4       62.96       55.56        66.67        59.26        66.67
5       76.67       66.67        70.00        70.00        73.33
6       78.57       50.00        78.57        78.57        78.57
7       88.00       96.00        84.00        84.00        84.00
8       58.33       70.83        58.33        54.17        54.17
9       73.33       73.33        63.33        63.33        63.33
====================================
Nilai Max keseluruhan fold :
====================================
1     65.62
2     77.27
3     85.29
4     63.33
5     66.67
6     76.67
7     78.57
8     96.00
9     70.83
10    73.33
dtype: float64

>>> Nilai maksimal keseluruhan fold :
8 = 96.0
====================================
Nilai rata-rata keseluruhan fold :
====================================
C45-recall     70.154
t-5 recall     66.692
t-10 recall    68.208
t-15 recall    67.366
t-20 recall    68.562
dtype: float64

>>> Nilai maksimal rata-rata keseluruhan fold :
C45-recall = 70.15400000000001


get_conclute_data(nk_acc_adb,nk_acc_c45)

******************
Nilai akurasi
******************
   C45-akurasi  t-5 akurasi  t-10 akurasi  t-15 akurasi  t-20 akurasi
0        63.64        63.64         71.43         71.43         71.43
1        71.43        76.62         77.92         77.92         77.92
2        74.03        76.62         79.22         79.22         79.22
3        62.34        66.23         67.53         67.53         67.53
4        76.62        76.62         79.22         79.22         79.22
5        75.32        77.92         79.22         80.52         81.82
6        72.73        76.62         74.03         75.32         75.32
7        80.52        81.82         88.31         88.31         88.31
8        72.73        72.73         72.73         74.03         74.03
9        77.33        81.33         80.00         80.00         80.00
====================================
Nilai Max keseluruhan fold :
====================================
1     71.43
2     77.92
3     79.22
4     67.53
5     79.22
6     81.82
7     76.62
8     88.31
9     74.03
10    81.33
dtype: float64

>>> Nilai maksimal keseluruhan fold :
8 = 88.31
====================================
Nilai rata-rata keseluruhan fold :
====================================
C45-akurasi     72.669
t-5 akurasi     75.015
t-10 akurasi    76.961
t-15 akurasi    77.350
t-20 akurasi    77.480
dtype: float64

>>> Nilai maksimal rata-rata keseluruhan fold :
t-20 akurasi = 77.47999999999999
******************
Nilai presisi
******************
   C45-presisi  t-5 presisi  t-10 presisi  t-15 presisi  t-20 presisi
0        59.09        57.14         69.23         70.83         70.83
1        50.00        57.69         64.71         64.71         64.71
2        68.42        71.05         82.14         82.14         82.14
3        51.72        56.67         60.87         60.87         60.87
4        69.57        69.57         76.19         76.19         76.19
5        68.97        72.41         81.82         85.71         90.00
6        36.00        42.86         33.33         36.84         36.84
7        69.23        72.00         83.33         83.33         83.33
8        56.00        55.17         57.89         61.11         61.11
9        76.00        80.77         82.61         82.61         82.61
====================================
Nilai Max keseluruhan fold :
====================================
1     70.83
2     64.71
3     82.14
4     60.87
5     76.19
6     90.00
7     42.86
8     83.33
9     61.11
10    82.61
dtype: float64

>>> Nilai maksimal keseluruhan fold :
6 = 90.0
====================================
Nilai rata-rata keseluruhan fold :
====================================
C45-presisi     60.500
t-5 presisi     63.533
t-10 presisi    69.212
t-15 presisi    70.434
t-20 presisi    70.863
dtype: float64

>>> Nilai maksimal rata-rata keseluruhan fold :
t-20 presisi = 70.863
******************
Nilai recall
******************
   C45-recall  t-5 recall  t-10 recall  t-15 recall  t-20 recall
0       40.62       50.00        56.25        53.12        53.12
1       54.55       68.18        50.00        50.00        50.00
2       76.47       79.41        67.65        67.65        67.65
3       50.00       56.67        46.67        46.67        46.67
4       59.26       59.26        59.26        59.26        59.26
5       66.67       70.00        60.00        60.00        60.00
6       64.29       85.71        42.86        50.00        50.00
7       72.00       72.00        80.00        80.00        80.00
8       58.33       66.67        45.83        45.83        45.83
9       63.33       70.00        63.33        63.33        63.33
====================================
Nilai Max keseluruhan fold :
====================================
1     56.25
2     68.18
3     79.41
4     56.67
5     59.26
6     70.00
7     85.71
8     80.00
9     66.67
10    70.00
dtype: float64

>>> Nilai maksimal keseluruhan fold :
7 = 85.71
====================================
Nilai rata-rata keseluruhan fold :
====================================
C45-recall     60.552
t-5 recall     67.790
t-10 recall    57.185
t-15 recall    57.586
t-20 recall    57.586
dtype: float64

>>> Nilai maksimal rata-rata keseluruhan fold :
t-5 recall = 67.78999999999999


adatree[0][0][0]

{'Glucose': {127: 0.0, 128: 1.0}}


adatree[0][0][1]

1.0889875752939042

manhax.github.io

IMPORT LIBRARY¶

FUNGSI PRA PROSES¶

INISIALISASI DATASET DAN SKENARIO DATASET¶

DATASET ASLI¶

DATASET SETELAH IMPUTASI¶

DATASET SETELAH DISKRITISASI TANPA IMPUTASI KNN¶

DATASET SETELAH DISKRITISASI DENGAN IMPUTASI KNN¶

FUNGSI ALGORITMA C4.5¶

FUNGSI AKURASI¶

FUNGSI ADABOOST¶

FUNGSI KFOLD¶

MENJALANKAN UJI SKENARIO¶

Dataset dengan imputasi K-NN¶

Dataset tanpa imputasi K-NN¶

FUNGSI UNTUK MENAMPILKAN HASIL¶

MENAMPILKAN HASIL¶

Dengan imputasi K-NN¶

Tanpa imputasi K-NN¶

LAMPIRAN¶

Contoh Hasil Rule C-45 pada Fold - 1¶

Contoh Hasil Rule Adaboost¶

Stump¶

Bobot¶

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	148	72	35	0	33.6	0.627	50	1
1	1	85	66	29	0	26.6	0.351	31	0
2	8	183	64	0	0	23.3	0.672	32	1
3	1	89	66	23	94	28.1	0.167	21	0
4	0	137	40	35	168	43.1	2.288	33	1
5	5	116	74	0	0	25.6	0.201	30	0
6	3	78	50	32	88	31.0	0.248	26	1
7	10	115	0	0	0	35.3	0.134	29	0
8	2	197	70	45	543	30.5	0.158	53	1
9	8	125	96	0	0	0.0	0.232	54	1
10	4	110	92	0	0	37.6	0.191	30	0
11	10	168	74	0	0	38.0	0.537	34	1
12	10	139	80	0	0	27.1	1.441	57	0
13	1	189	60	23	846	30.1	0.398	59	1
14	5	166	72	19	175	25.8	0.587	51	1
15	7	100	0	0	0	30.0	0.484	32	1
16	0	118	84	47	230	45.8	0.551	31	1
17	7	107	74	0	0	29.6	0.254	31	1
18	1	103	30	38	83	43.3	0.183	33	0
19	1	115	70	30	96	34.6	0.529	32	1
20	3	126	88	41	235	39.3	0.704	27	0
21	8	99	84	0	0	35.4	0.388	50	0
22	7	196	90	0	0	39.8	0.451	41	1
23	9	119	80	35	0	29.0	0.263	29	1
24	11	143	94	33	146	36.6	0.254	51	1
25	10	125	70	26	115	31.1	0.205	41	1
26	7	147	76	0	0	39.4	0.257	43	1
27	1	97	66	15	140	23.2	0.487	22	0
28	13	145	82	19	110	22.2	0.245	57	0
29	5	117	92	0	0	34.1	0.337	38	0
30	5	109	75	26	0	36.0	0.546	60	0
31	3	158	76	36	245	31.6	0.851	28	1
32	3	88	58	11	54	24.8	0.267	22	0
33	6	92	92	0	0	19.9	0.188	28	0
34	10	122	78	31	0	27.6	0.512	45	0
35	4	103	60	33	192	24.0	0.966	33	0
36	11	138	76	0	0	33.2	0.420	35	0
37	9	102	76	37	0	32.9	0.665	46	1
38	2	90	68	42	0	38.2	0.503	27	1
39	4	111	72	47	207	37.1	1.390	56	1
40	3	180	64	25	70	34.0	0.271	26	0
41	7	133	84	0	0	40.2	0.696	37	0
42	7	106	92	18	0	22.7	0.235	48	0
43	9	171	110	24	240	45.4	0.721	54	1
44	7	159	64	0	0	27.4	0.294	40	0
45	0	180	66	39	0	42.0	1.893	25	1
46	1	146	56	0	0	29.7	0.564	29	0
47	2	71	70	27	0	28.0	0.586	22	0
48	7	103	66	32	0	39.1	0.344	31	1
49	7	105	0	0	0	0.0	0.305	24	0
50	1	103	80	11	82	19.4	0.491	22	0
51	1	101	50	15	36	24.2	0.526	26	0
52	5	88	66	21	23	24.4	0.342	30	0
53	8	176	90	34	300	33.7	0.467	58	1
54	7	150	66	42	342	34.7	0.718	42	0
55	1	73	50	10	0	23.0	0.248	21	0
56	7	187	68	39	304	37.7	0.254	41	1
57	0	100	88	60	110	46.8	0.962	31	0
58	0	146	82	0	0	40.5	1.781	44	0
59	0	105	64	41	142	41.5	0.173	22	0
60	2	84	0	0	0	0.0	0.304	21	0
61	8	133	72	0	0	32.9	0.270	39	1
62	5	44	62	0	0	25.0	0.587	36	0
63	2	141	58	34	128	25.4	0.699	24	0
64	7	114	66	0	0	32.8	0.258	42	1
65	5	99	74	27	0	29.0	0.203	32	0
66	0	109	88	30	0	32.5	0.855	38	1
67	2	109	92	0	0	42.7	0.845	54	0
68	1	95	66	13	38	19.6	0.334	25	0
69	4	146	85	27	100	28.9	0.189	27	0
70	2	100	66	20	90	32.9	0.867	28	1
71	5	139	64	35	140	28.6	0.411	26	0
72	13	126	90	0	0	43.4	0.583	42	1
73	4	129	86	20	270	35.1	0.231	23	0
74	1	79	75	30	0	32.0	0.396	22	0
75	1	0	48	20	0	24.7	0.140	22	0
76	7	62	78	0	0	32.6	0.391	41	0
77	5	95	72	33	0	37.7	0.370	27	0
78	0	131	0	0	0	43.2	0.270	26	1
79	2	112	66	22	0	25.0	0.307	24	0
80	3	113	44	13	0	22.4	0.140	22	0
81	2	74	0	0	0	0.0	0.102	22	0
82	7	83	78	26	71	29.3	0.767	36	0
83	0	101	65	28	0	24.6	0.237	22	0
84	5	137	108	0	0	48.8	0.227	37	1
85	2	110	74	29	125	32.4	0.698	27	0
86	13	106	72	54	0	36.6	0.178	45	0
87	2	100	68	25	71	38.5	0.324	26	0
88	15	136	70	32	110	37.1	0.153	43	1
89	1	107	68	19	0	26.5	0.165	24	0
90	1	80	55	0	0	19.1	0.258	21	0
91	4	123	80	15	176	32.0	0.443	34	0
92	7	81	78	40	48	46.7	0.261	42	0
93	4	134	72	0	0	23.8	0.277	60	1
94	2	142	82	18	64	24.7	0.761	21	0
95	6	144	72	27	228	33.9	0.255	40	0
96	2	92	62	28	0	31.6	0.130	24	0
97	1	71	48	18	76	20.4	0.323	22	0
98	6	93	50	30	64	28.7	0.356	23	0
99	1	122	90	51	220	49.7	0.325	31	1
100	1	163	72	0	0	39.0	1.222	33	1
101	1	151	60	0	0	26.1	0.179	22	0
102	0	125	96	0	0	22.5	0.262	21	0
103	1	81	72	18	40	26.6	0.283	24	0
104	2	85	65	0	0	39.6	0.930	27	0
105	1	126	56	29	152	28.7	0.801	21	0
106	1	96	122	0	0	22.4	0.207	27	0
107	4	144	58	28	140	29.5	0.287	37	0
108	3	83	58	31	18	34.3	0.336	25	0
109	0	95	85	25	36	37.4	0.247	24	1
110	3	171	72	33	135	33.3	0.199	24	1
111	8	155	62	26	495	34.0	0.543	46	1
112	1	89	76	34	37	31.2	0.192	23	0
113	4	76	62	0	0	34.0	0.391	25	0
114	7	160	54	32	175	30.5	0.588	39	1
115	4	146	92	0	0	31.2	0.539	61	1
116	5	124	74	0	0	34.0	0.220	38	1
117	5	78	48	0	0	33.7	0.654	25	0
118	4	97	60	23	0	28.2	0.443	22	0
119	4	99	76	15	51	23.2	0.223	21	0
120	0	162	76	56	100	53.2	0.759	25	1
121	6	111	64	39	0	34.2	0.260	24	0
122	2	107	74	30	100	33.6	0.404	23	0
123	5	132	80	0	0	26.8	0.186	69	0
124	0	113	76	0	0	33.3	0.278	23	1
125	1	88	30	42	99	55.0	0.496	26	1
126	3	120	70	30	135	42.9	0.452	30	0
127	1	118	58	36	94	33.3	0.261	23	0
128	1	117	88	24	145	34.5	0.403	40	1
129	0	105	84	0	0	27.9	0.741	62	1
130	4	173	70	14	168	29.7	0.361	33	1
131	9	122	56	0	0	33.3	1.114	33	1
132	3	170	64	37	225	34.5	0.356	30	1
133	8	84	74	31	0	38.3	0.457	39	0
134	2	96	68	13	49	21.1	0.647	26	0
135	2	125	60	20	140	33.8	0.088	31	0
136	0	100	70	26	50	30.8	0.597	21	0
137	0	93	60	25	92	28.7	0.532	22	0
138	0	129	80	0	0	31.2	0.703	29	0
139	5	105	72	29	325	36.9	0.159	28	0
140	3	128	78	0	0	21.1	0.268	55	0
141	5	106	82	30	0	39.5	0.286	38	0
142	2	108	52	26	63	32.5	0.318	22	0
143	10	108	66	0	0	32.4	0.272	42	1
144	4	154	62	31	284	32.8	0.237	23	0
145	0	102	75	23	0	0.0	0.572	21	0
146	9	57	80	37	0	32.8	0.096	41	0
147	2	106	64	35	119	30.5	1.400	34	0
148	5	147	78	0	0	33.7	0.218	65	0
149	2	90	70	17	0	27.3	0.085	22	0
150	1	136	74	50	204	37.4	0.399	24	0
151	4	114	65	0	0	21.9	0.432	37	0
152	9	156	86	28	155	34.3	1.189	42	1
153	1	153	82	42	485	40.6	0.687	23	0
154	8	188	78	0	0	47.9	0.137	43	1
155	7	152	88	44	0	50.0	0.337	36	1
156	2	99	52	15	94	24.6	0.637	21	0
157	1	109	56	21	135	25.2	0.833	23	0
158	2	88	74	19	53	29.0	0.229	22	0
159	17	163	72	41	114	40.9	0.817	47	1
160	4	151	90	38	0	29.7	0.294	36	0
161	7	102	74	40	105	37.2	0.204	45	0
162	0	114	80	34	285	44.2	0.167	27	0
163	2	100	64	23	0	29.7	0.368	21	0
164	0	131	88	0	0	31.6	0.743	32	1
165	6	104	74	18	156	29.9	0.722	41	1
166	3	148	66	25	0	32.5	0.256	22	0
167	4	120	68	0	0	29.6	0.709	34	0
168	4	110	66	0	0	31.9	0.471	29	0
169	3	111	90	12	78	28.4	0.495	29	0
170	6	102	82	0	0	30.8	0.180	36	1
171	6	134	70	23	130	35.4	0.542	29	1
172	2	87	0	23	0	28.9	0.773	25	0
173	1	79	60	42	48	43.5	0.678	23	0
174	2	75	64	24	55	29.7	0.370	33	0
175	8	179	72	42	130	32.7	0.719	36	1
176	6	85	78	0	0	31.2	0.382	42	0
177	0	129	110	46	130	67.1	0.319	26	1
178	5	143	78	0	0	45.0	0.190	47	0
179	5	130	82	0	0	39.1	0.956	37	1
180	6	87	80	0	0	23.2	0.084	32	0
181	0	119	64	18	92	34.9	0.725	23	0
182	1	0	74	20	23	27.7	0.299	21	0
183	5	73	60	0	0	26.8	0.268	27	0
184	4	141	74	0	0	27.6	0.244	40	0
185	7	194	68	28	0	35.9	0.745	41	1
186	8	181	68	36	495	30.1	0.615	60	1
187	1	128	98	41	58	32.0	1.321	33	1
188	8	109	76	39	114	27.9	0.640	31	1
189	5	139	80	35	160	31.6	0.361	25	1
190	3	111	62	0	0	22.6	0.142	21	0
191	9	123	70	44	94	33.1	0.374	40	0
192	7	159	66	0	0	30.4	0.383	36	1
193	11	135	0	0	0	52.3	0.578	40	1
194	8	85	55	20	0	24.4	0.136	42	0
195	5	158	84	41	210	39.4	0.395	29	1
196	1	105	58	0	0	24.3	0.187	21	0
197	3	107	62	13	48	22.9	0.678	23	1
198	4	109	64	44	99	34.8	0.905	26	1
199	4	148	60	27	318	30.9	0.150	29	1
200	0	113	80	16	0	31.0	0.874	21	0
201	1	138	82	0	0	40.1	0.236	28	0
202	0	108	68	20	0	27.3	0.787	32	0
203	2	99	70	16	44	20.4	0.235	27	0
204	6	103	72	32	190	37.7	0.324	55	0
205	5	111	72	28	0	23.9	0.407	27	0
206	8	196	76	29	280	37.5	0.605	57	1
207	5	162	104	0	0	37.7	0.151	52	1
208	1	96	64	27	87	33.2	0.289	21	0
209	7	184	84	33	0	35.5	0.355	41	1
210	2	81	60	22	0	27.7	0.290	25	0
211	0	147	85	54	0	42.8	0.375	24	0
212	7	179	95	31	0	34.2	0.164	60	0
213	0	140	65	26	130	42.6	0.431	24	1
214	9	112	82	32	175	34.2	0.260	36	1
215	12	151	70	40	271	41.8	0.742	38	1
216	5	109	62	41	129	35.8	0.514	25	1
217	6	125	68	30	120	30.0	0.464	32	0
218	5	85	74	22	0	29.0	1.224	32	1
219	5	112	66	0	0	37.8	0.261	41	1
220	0	177	60	29	478	34.6	1.072	21	1
221	2	158	90	0	0	31.6	0.805	66	1
222	7	119	0	0	0	25.2	0.209	37	0
223	7	142	60	33	190	28.8	0.687	61	0
224	1	100	66	15	56	23.6	0.666	26	0
225	1	87	78	27	32	34.6	0.101	22	0
226	0	101	76	0	0	35.7	0.198	26	0
227	3	162	52	38	0	37.2	0.652	24	1
228	4	197	70	39	744	36.7	2.329	31	0
229	0	117	80	31	53	45.2	0.089	24	0
230	4	142	86	0	0	44.0	0.645	22	1
231	6	134	80	37	370	46.2	0.238	46	1
232	1	79	80	25	37	25.4	0.583	22	0
233	4	122	68	0	0	35.0	0.394	29	0
234	3	74	68	28	45	29.7	0.293	23	0
235	4	171	72	0	0	43.6	0.479	26	1
236	7	181	84	21	192	35.9	0.586	51	1
237	0	179	90	27	0	44.1	0.686	23	1
238	9	164	84	21	0	30.8	0.831	32	1
239	0	104	76	0	0	18.4	0.582	27	0
240	1	91	64	24	0	29.2	0.192	21	0
241	4	91	70	32	88	33.1	0.446	22	0
242	3	139	54	0	0	25.6	0.402	22	1
243	6	119	50	22	176	27.1	1.318	33	1
244	2	146	76	35	194	38.2	0.329	29	0
245	9	184	85	15	0	30.0	1.213	49	1
246	10	122	68	0	0	31.2	0.258	41	0
247	0	165	90	33	680	52.3	0.427	23	0
248	9	124	70	33	402	35.4	0.282	34	0
249	1	111	86	19	0	30.1	0.143	23	0
250	9	106	52	0	0	31.2	0.380	42	0
251	2	129	84	0	0	28.0	0.284	27	0
252	2	90	80	14	55	24.4	0.249	24	0
253	0	86	68	32	0	35.8	0.238	25	0
254	12	92	62	7	258	27.6	0.926	44	1
255	1	113	64	35	0	33.6	0.543	21	1
256	3	111	56	39	0	30.1	0.557	30	0
257	2	114	68	22	0	28.7	0.092	25	0
258	1	193	50	16	375	25.9	0.655	24	0
259	11	155	76	28	150	33.3	1.353	51	1
260	3	191	68	15	130	30.9	0.299	34	0
261	3	141	0	0	0	30.0	0.761	27	1
262	4	95	70	32	0	32.1	0.612	24	0
263	3	142	80	15	0	32.4	0.200	63	0
264	4	123	62	0	0	32.0	0.226	35	1
265	5	96	74	18	67	33.6	0.997	43	0
266	0	138	0	0	0	36.3	0.933	25	1
267	2	128	64	42	0	40.0	1.101	24	0
268	0	102	52	0	0	25.1	0.078	21	0
269	2	146	0	0	0	27.5	0.240	28	1
270	10	101	86	37	0	45.6	1.136	38	1
271	2	108	62	32	56	25.2	0.128	21	0
272	3	122	78	0	0	23.0	0.254	40	0
273	1	71	78	50	45	33.2	0.422	21	0
274	13	106	70	0	0	34.2	0.251	52	0
275	2	100	70	52	57	40.5	0.677	25	0
276	7	106	60	24	0	26.5	0.296	29	1
277	0	104	64	23	116	27.8	0.454	23	0
278	5	114	74	0	0	24.9	0.744	57	0
279	2	108	62	10	278	25.3	0.881	22	0
280	0	146	70	0	0	37.9	0.334	28	1
281	10	129	76	28	122	35.9	0.280	39	0
282	7	133	88	15	155	32.4	0.262	37	0
283	7	161	86	0	0	30.4	0.165	47	1
284	2	108	80	0	0	27.0	0.259	52	1
285	7	136	74	26	135	26.0	0.647	51	0
286	5	155	84	44	545	38.7	0.619	34	0
287	1	119	86	39	220	45.6	0.808	29	1
288	4	96	56	17	49	20.8	0.340	26	0
289	5	108	72	43	75	36.1	0.263	33	0
290	0	78	88	29	40	36.9	0.434	21	0
291	0	107	62	30	74	36.6	0.757	25	1
292	2	128	78	37	182	43.3	1.224	31	1
293	1	128	48	45	194	40.5	0.613	24	1
294	0	161	50	0	0	21.9	0.254	65	0
295	6	151	62	31	120	35.5	0.692	28	0
296	2	146	70	38	360	28.0	0.337	29	1
297	0	126	84	29	215	30.7	0.520	24	0
298	14	100	78	25	184	36.6	0.412	46	1
299	8	112	72	0	0	23.6	0.840	58	0
300	0	167	0	0	0	32.3	0.839	30	1
301	2	144	58	33	135	31.6	0.422	25	1
302	5	77	82	41	42	35.8	0.156	35	0
303	5	115	98	0	0	52.9	0.209	28	1
304	3	150	76	0	0	21.0	0.207	37	0
305	2	120	76	37	105	39.7	0.215	29	0
306	10	161	68	23	132	25.5	0.326	47	1
307	0	137	68	14	148	24.8	0.143	21	0
308	0	128	68	19	180	30.5	1.391	25	1
309	2	124	68	28	205	32.9	0.875	30	1
310	6	80	66	30	0	26.2	0.313	41	0
311	0	106	70	37	148	39.4	0.605	22	0
312	2	155	74	17	96	26.6	0.433	27	1
313	3	113	50	10	85	29.5	0.626	25	0
314	7	109	80	31	0	35.9	1.127	43	1
315	2	112	68	22	94	34.1	0.315	26	0
316	3	99	80	11	64	19.3	0.284	30	0
317	3	182	74	0	0	30.5	0.345	29	1
318	3	115	66	39	140	38.1	0.150	28	0
319	6	194	78	0	0	23.5	0.129	59	1
320	4	129	60	12	231	27.5	0.527	31	0
321	3	112	74	30	0	31.6	0.197	25	1
322	0	124	70	20	0	27.4	0.254	36	1
323	13	152	90	33	29	26.8	0.731	43	1
324	2	112	75	32	0	35.7	0.148	21	0
325	1	157	72	21	168	25.6	0.123	24	0
326	1	122	64	32	156	35.1	0.692	30	1
327	10	179	70	0	0	35.1	0.200	37	0
328	2	102	86	36	120	45.5	0.127	23	1
329	6	105	70	32	68	30.8	0.122	37	0
330	8	118	72	19	0	23.1	1.476	46	0
331	2	87	58	16	52	32.7	0.166	25	0
332	1	180	0	0	0	43.3	0.282	41	1
333	12	106	80	0	0	23.6	0.137	44	0
334	1	95	60	18	58	23.9	0.260	22	0
335	0	165	76	43	255	47.9	0.259	26	0
336	0	117	0	0	0	33.8	0.932	44	0
337	5	115	76	0	0	31.2	0.343	44	1
338	9	152	78	34	171	34.2	0.893	33	1
339	7	178	84	0	0	39.9	0.331	41	1
340	1	130	70	13	105	25.9	0.472	22	0
341	1	95	74	21	73	25.9	0.673	36	0
342	1	0	68	35	0	32.0	0.389	22	0
343	5	122	86	0	0	34.7	0.290	33	0
344	8	95	72	0	0	36.8	0.485	57	0
345	8	126	88	36	108	38.5	0.349	49	0
346	1	139	46	19	83	28.7	0.654	22	0
347	3	116	0	0	0	23.5	0.187	23	0
348	3	99	62	19	74	21.8	0.279	26	0
349	5	0	80	32	0	41.0	0.346	37	1
350	4	92	80	0	0	42.2	0.237	29	0
351	4	137	84	0	0	31.2	0.252	30	0
352	3	61	82	28	0	34.4	0.243	46	0
353	1	90	62	12	43	27.2	0.580	24	0
354	3	90	78	0	0	42.7	0.559	21	0
355	9	165	88	0	0	30.4	0.302	49	1
356	1	125	50	40	167	33.3	0.962	28	1
357	13	129	0	30	0	39.9	0.569	44	1
358	12	88	74	40	54	35.3	0.378	48	0
359	1	196	76	36	249	36.5	0.875	29	1
360	5	189	64	33	325	31.2	0.583	29	1
361	5	158	70	0	0	29.8	0.207	63	0
362	5	103	108	37	0	39.2	0.305	65	0
363	4	146	78	0	0	38.5	0.520	67	1
364	4	147	74	25	293	34.9	0.385	30	0
365	5	99	54	28	83	34.0	0.499	30	0
366	6	124	72	0	0	27.6	0.368	29	1
367	0	101	64	17	0	21.0	0.252	21	0
368	3	81	86	16	66	27.5	0.306	22	0
369	1	133	102	28	140	32.8	0.234	45	1
370	3	173	82	48	465	38.4	2.137	25	1
371	0	118	64	23	89	0.0	1.731	21	0
372	0	84	64	22	66	35.8	0.545	21	0
373	2	105	58	40	94	34.9	0.225	25	0
374	2	122	52	43	158	36.2	0.816	28	0
375	12	140	82	43	325	39.2	0.528	58	1
376	0	98	82	15	84	25.2	0.299	22	0
377	1	87	60	37	75	37.2	0.509	22	0
378	4	156	75	0	0	48.3	0.238	32	1
379	0	93	100	39	72	43.4	1.021	35	0
380	1	107	72	30	82	30.8	0.821	24	0
381	0	105	68	22	0	20.0	0.236	22	0
382	1	109	60	8	182	25.4	0.947	21	0
383	1	90	62	18	59	25.1	1.268	25	0
384	1	125	70	24	110	24.3	0.221	25	0
385	1	119	54	13	50	22.3	0.205	24	0
386	5	116	74	29	0	32.3	0.660	35	1
387	8	105	100	36	0	43.3	0.239	45	1
388	5	144	82	26	285	32.0	0.452	58	1
389	3	100	68	23	81	31.6	0.949	28	0
390	1	100	66	29	196	32.0	0.444	42	0
391	5	166	76	0	0	45.7	0.340	27	1
392	1	131	64	14	415	23.7	0.389	21	0
393	4	116	72	12	87	22.1	0.463	37	0
394	4	158	78	0	0	32.9	0.803	31	1
395	2	127	58	24	275	27.7	1.600	25	0
396	3	96	56	34	115	24.7	0.944	39	0
397	0	131	66	40	0	34.3	0.196	22	1
398	3	82	70	0	0	21.1	0.389	25	0
399	3	193	70	31	0	34.9	0.241	25	1
400	4	95	64	0	0	32.0	0.161	31	1
401	6	137	61	0	0	24.2	0.151	55	0
402	5	136	84	41	88	35.0	0.286	35	1
403	9	72	78	25	0	31.6	0.280	38	0
404	5	168	64	0	0	32.9	0.135	41	1
405	2	123	48	32	165	42.1	0.520	26	0
406	4	115	72	0	0	28.9	0.376	46	1
407	0	101	62	0	0	21.9	0.336	25	0
408	8	197	74	0	0	25.9	1.191	39	1
409	1	172	68	49	579	42.4	0.702	28	1
410	6	102	90	39	0	35.7	0.674	28	0
411	1	112	72	30	176	34.4	0.528	25	0
412	1	143	84	23	310	42.4	1.076	22	0
413	1	143	74	22	61	26.2	0.256	21	0
414	0	138	60	35	167	34.6	0.534	21	1
415	3	173	84	33	474	35.7	0.258	22	1
416	1	97	68	21	0	27.2	1.095	22	0
417	4	144	82	32	0	38.5	0.554	37	1
418	1	83	68	0	0	18.2	0.624	27	0
419	3	129	64	29	115	26.4	0.219	28	1
420	1	119	88	41	170	45.3	0.507	26	0
421	2	94	68	18	76	26.0	0.561	21	0
422	0	102	64	46	78	40.6	0.496	21	0
423	2	115	64	22	0	30.8	0.421	21	0
424	8	151	78	32	210	42.9	0.516	36	1
425	4	184	78	39	277	37.0	0.264	31	1
426	0	94	0	0	0	0.0	0.256	25	0
427	1	181	64	30	180	34.1	0.328	38	1
428	0	135	94	46	145	40.6	0.284	26	0
429	1	95	82	25	180	35.0	0.233	43	1
430	2	99	0	0	0	22.2	0.108	23	0
431	3	89	74	16	85	30.4	0.551	38	0
432	1	80	74	11	60	30.0	0.527	22	0
433	2	139	75	0	0	25.6	0.167	29	0
434	1	90	68	8	0	24.5	1.138	36	0
435	0	141	0	0	0	42.4	0.205	29	1
436	12	140	85	33	0	37.4	0.244	41	0
437	5	147	75	0	0	29.9	0.434	28	0
438	1	97	70	15	0	18.2	0.147	21	0
439	6	107	88	0	0	36.8	0.727	31	0
440	0	189	104	25	0	34.3	0.435	41	1
441	2	83	66	23	50	32.2	0.497	22	0
442	4	117	64	27	120	33.2	0.230	24	0
443	8	108	70	0	0	30.5	0.955	33	1
444	4	117	62	12	0	29.7	0.380	30	1
445	0	180	78	63	14	59.4	2.420	25	1
446	1	100	72	12	70	25.3	0.658	28	0
447	0	95	80	45	92	36.5	0.330	26	0
448	0	104	64	37	64	33.6	0.510	22	1
449	0	120	74	18	63	30.5	0.285	26	0
450	1	82	64	13	95	21.2	0.415	23	0
451	2	134	70	0	0	28.9	0.542	23	1
452	0	91	68	32	210	39.9	0.381	25	0
453	2	119	0	0	0	19.6	0.832	72	0
454	2	100	54	28	105	37.8	0.498	24	0
455	14	175	62	30	0	33.6	0.212	38	1
456	1	135	54	0	0	26.7	0.687	62	0
457	5	86	68	28	71	30.2	0.364	24	0
458	10	148	84	48	237	37.6	1.001	51	1
459	9	134	74	33	60	25.9	0.460	81	0
460	9	120	72	22	56	20.8	0.733	48	0
461	1	71	62	0	0	21.8	0.416	26	0
462	8	74	70	40	49	35.3	0.705	39	0
463	5	88	78	30	0	27.6	0.258	37	0
464	10	115	98	0	0	24.0	1.022	34	0
465	0	124	56	13	105	21.8	0.452	21	0
466	0	74	52	10	36	27.8	0.269	22	0
467	0	97	64	36	100	36.8	0.600	25	0
468	8	120	0	0	0	30.0	0.183	38	1
469	6	154	78	41	140	46.1	0.571	27	0
470	1	144	82	40	0	41.3	0.607	28	0
471	0	137	70	38	0	33.2	0.170	22	0
472	0	119	66	27	0	38.8	0.259	22	0
473	7	136	90	0	0	29.9	0.210	50	0
474	4	114	64	0	0	28.9	0.126	24	0
475	0	137	84	27	0	27.3	0.231	59	0
476	2	105	80	45	191	33.7	0.711	29	1
477	7	114	76	17	110	23.8	0.466	31	0
478	8	126	74	38	75	25.9	0.162	39	0
479	4	132	86	31	0	28.0	0.419	63	0
480	3	158	70	30	328	35.5	0.344	35	1
481	0	123	88	37	0	35.2	0.197	29	0
482	4	85	58	22	49	27.8	0.306	28	0
483	0	84	82	31	125	38.2	0.233	23	0
484	0	145	0	0	0	44.2	0.630	31	1
485	0	135	68	42	250	42.3	0.365	24	1
486	1	139	62	41	480	40.7	0.536	21	0
487	0	173	78	32	265	46.5	1.159	58	0
488	4	99	72	17	0	25.6	0.294	28	0
489	8	194	80	0	0	26.1	0.551	67	0
490	2	83	65	28	66	36.8	0.629	24	0
491	2	89	90	30	0	33.5	0.292	42	0
492	4	99	68	38	0	32.8	0.145	33	0
493	4	125	70	18	122	28.9	1.144	45	1
494	3	80	0	0	0	0.0	0.174	22	0
495	6	166	74	0	0	26.6	0.304	66	0
496	5	110	68	0	0	26.0	0.292	30	0
497	2	81	72	15	76	30.1	0.547	25	0
498	7	195	70	33	145	25.1	0.163	55	1
499	6	154	74	32	193	29.3	0.839	39	0
500	2	117	90	19	71	25.2	0.313	21	0
501	3	84	72	32	0	37.2	0.267	28	0
502	6	0	68	41	0	39.0	0.727	41	1
503	7	94	64	25	79	33.3	0.738	41	0
504	3	96	78	39	0	37.3	0.238	40	0
505	10	75	82	0	0	33.3	0.263	38	0
506	0	180	90	26	90	36.5	0.314	35	1
507	1	130	60	23	170	28.6	0.692	21	0
508	2	84	50	23	76	30.4	0.968	21	0
509	8	120	78	0	0	25.0	0.409	64	0
510	12	84	72	31	0	29.7	0.297	46	1
511	0	139	62	17	210	22.1	0.207	21	0
512	9	91	68	0	0	24.2	0.200	58	0
513	2	91	62	0	0	27.3	0.525	22	0
514	3	99	54	19	86	25.6	0.154	24	0
515	3	163	70	18	105	31.6	0.268	28	1
516	9	145	88	34	165	30.3	0.771	53	1
517	7	125	86	0	0	37.6	0.304	51	0
518	13	76	60	0	0	32.8	0.180	41	0
519	6	129	90	7	326	19.6	0.582	60	0
520	2	68	70	32	66	25.0	0.187	25	0
521	3	124	80	33	130	33.2	0.305	26	0
522	6	114	0	0	0	0.0	0.189	26	0
523	9	130	70	0	0	34.2	0.652	45	1
524	3	125	58	0	0	31.6	0.151	24	0
525	3	87	60	18	0	21.8	0.444	21	0
526	1	97	64	19	82	18.2	0.299	21	0
527	3	116	74	15	105	26.3	0.107	24	0
528	0	117	66	31	188	30.8	0.493	22	0
529	0	111	65	0	0	24.6	0.660	31	0
530	2	122	60	18	106	29.8	0.717	22	0
531	0	107	76	0	0	45.3	0.686	24	0
532	1	86	66	52	65	41.3	0.917	29	0
533	6	91	0	0	0	29.8	0.501	31	0
534	1	77	56	30	56	33.3	1.251	24	0
535	4	132	0	0	0	32.9	0.302	23	1
536	0	105	90	0	0	29.6	0.197	46	0
537	0	57	60	0	0	21.7	0.735	67	0
538	0	127	80	37	210	36.3	0.804	23	0
539	3	129	92	49	155	36.4	0.968	32	1
540	8	100	74	40	215	39.4	0.661	43	1
541	3	128	72	25	190	32.4	0.549	27	1
542	10	90	85	32	0	34.9	0.825	56	1
543	4	84	90	23	56	39.5	0.159	25	0
544	1	88	78	29	76	32.0	0.365	29	0
545	8	186	90	35	225	34.5	0.423	37	1
546	5	187	76	27	207	43.6	1.034	53	1
547	4	131	68	21	166	33.1	0.160	28	0
548	1	164	82	43	67	32.8	0.341	50	0
549	4	189	110	31	0	28.5	0.680	37	0
550	1	116	70	28	0	27.4	0.204	21	0
551	3	84	68	30	106	31.9	0.591	25	0
552	6	114	88	0	0	27.8	0.247	66	0
553	1	88	62	24	44	29.9	0.422	23	0
554	1	84	64	23	115	36.9	0.471	28	0
555	7	124	70	33	215	25.5	0.161	37	0
556	1	97	70	40	0	38.1	0.218	30	0
557	8	110	76	0	0	27.8	0.237	58	0
558	11	103	68	40	0	46.2	0.126	42	0
559	11	85	74	0	0	30.1	0.300	35	0
560	6	125	76	0	0	33.8	0.121	54	1
561	0	198	66	32	274	41.3	0.502	28	1
562	1	87	68	34	77	37.6	0.401	24	0
563	6	99	60	19	54	26.9	0.497	32	0
564	0	91	80	0	0	32.4	0.601	27	0
565	2	95	54	14	88	26.1	0.748	22	0
566	1	99	72	30	18	38.6	0.412	21	0
567	6	92	62	32	126	32.0	0.085	46	0
568	4	154	72	29	126	31.3	0.338	37	0
569	0	121	66	30	165	34.3	0.203	33	1
570	3	78	70	0	0	32.5	0.270	39	0
571	2	130	96	0	0	22.6	0.268	21	0
572	3	111	58	31	44	29.5	0.430	22	0
573	2	98	60	17	120	34.7	0.198	22	0
574	1	143	86	30	330	30.1	0.892	23	0
575	1	119	44	47	63	35.5	0.280	25	0
576	6	108	44	20	130	24.0	0.813	35	0
577	2	118	80	0	0	42.9	0.693	21	1
578	10	133	68	0	0	27.0	0.245	36	0
579	2	197	70	99	0	34.7	0.575	62	1
580	0	151	90	46	0	42.1	0.371	21	1
581	6	109	60	27	0	25.0	0.206	27	0
582	12	121	78	17	0	26.5	0.259	62	0
583	8	100	76	0	0	38.7	0.190	42	0
584	8	124	76	24	600	28.7	0.687	52	1
585	1	93	56	11	0	22.5	0.417	22	0
586	8	143	66	0	0	34.9	0.129	41	1
587	6	103	66	0	0	24.3	0.249	29	0
588	3	176	86	27	156	33.3	1.154	52	1
589	0	73	0	0	0	21.1	0.342	25	0
590	11	111	84	40	0	46.8	0.925	45	1
591	2	112	78	50	140	39.4	0.175	24	0
592	3	132	80	0	0	34.4	0.402	44	1
593	2	82	52	22	115	28.5	1.699	25	0
594	6	123	72	45	230	33.6	0.733	34	0
595	0	188	82	14	185	32.0	0.682	22	1
596	0	67	76	0	0	45.3	0.194	46	0
597	1	89	24	19	25	27.8	0.559	21	0
598	1	173	74	0	0	36.8	0.088	38	1
599	1	109	38	18	120	23.1	0.407	26	0
600	1	108	88	19	0	27.1	0.400	24	0
601	6	96	0	0	0	23.7	0.190	28	0
602	1	124	74	36	0	27.8	0.100	30	0
603	7	150	78	29	126	35.2	0.692	54	1
604	4	183	0	0	0	28.4	0.212	36	1
605	1	124	60	32	0	35.8	0.514	21	0
606	1	181	78	42	293	40.0	1.258	22	1
607	1	92	62	25	41	19.5	0.482	25	0
608	0	152	82	39	272	41.5	0.270	27	0
609	1	111	62	13	182	24.0	0.138	23	0
610	3	106	54	21	158	30.9	0.292	24	0
611	3	174	58	22	194	32.9	0.593	36	1
612	7	168	88	42	321	38.2	0.787	40	1
613	6	105	80	28	0	32.5	0.878	26	0
614	11	138	74	26	144	36.1	0.557	50	1
615	3	106	72	0	0	25.8	0.207	27	0
616	6	117	96	0	0	28.7	0.157	30	0
617	2	68	62	13	15	20.1	0.257	23	0
618	9	112	82	24	0	28.2	1.282	50	1
619	0	119	0	0	0	32.4	0.141	24	1
620	2	112	86	42	160	38.4	0.246	28	0
621	2	92	76	20	0	24.2	1.698	28	0
622	6	183	94	0	0	40.8	1.461	45	0
623	0	94	70	27	115	43.5	0.347	21	0
624	2	108	64	0	0	30.8	0.158	21	0
625	4	90	88	47	54	37.7	0.362	29	0
626	0	125	68	0	0	24.7	0.206	21	0
627	0	132	78	0	0	32.4	0.393	21	0
628	5	128	80	0	0	34.6	0.144	45	0
629	4	94	65	22	0	24.7	0.148	21	0
630	7	114	64	0	0	27.4	0.732	34	1
631	0	102	78	40	90	34.5	0.238	24	0
632	2	111	60	0	0	26.2	0.343	23	0
633	1	128	82	17	183	27.5	0.115	22	0
634	10	92	62	0	0	25.9	0.167	31	0
635	13	104	72	0	0	31.2	0.465	38	1
636	5	104	74	0	0	28.8	0.153	48	0
637	2	94	76	18	66	31.6	0.649	23	0
638	7	97	76	32	91	40.9	0.871	32	1
639	1	100	74	12	46	19.5	0.149	28	0
640	0	102	86	17	105	29.3	0.695	27	0
641	4	128	70	0	0	34.3	0.303	24	0
642	6	147	80	0	0	29.5	0.178	50	1
643	4	90	0	0	0	28.0	0.610	31	0
644	3	103	72	30	152	27.6	0.730	27	0
645	2	157	74	35	440	39.4	0.134	30	0
646	1	167	74	17	144	23.4	0.447	33	1
647	0	179	50	36	159	37.8	0.455	22	1
648	11	136	84	35	130	28.3	0.260	42	1
649	0	107	60	25	0	26.4	0.133	23	0
650	1	91	54	25	100	25.2	0.234	23	0
651	1	117	60	23	106	33.8	0.466	27	0
652	5	123	74	40	77	34.1	0.269	28	0
653	2	120	54	0	0	26.8	0.455	27	0
654	1	106	70	28	135	34.2	0.142	22	0
655	2	155	52	27	540	38.7	0.240	25	1
656	2	101	58	35	90	21.8	0.155	22	0
657	1	120	80	48	200	38.9	1.162	41	0
658	11	127	106	0	0	39.0	0.190	51	0
659	3	80	82	31	70	34.2	1.292	27	1
660	10	162	84	0	0	27.7	0.182	54	0
661	1	199	76	43	0	42.9	1.394	22	1
662	8	167	106	46	231	37.6	0.165	43	1
663	9	145	80	46	130	37.9	0.637	40	1
664	6	115	60	39	0	33.7	0.245	40	1
665	1	112	80	45	132	34.8	0.217	24	0
666	4	145	82	18	0	32.5	0.235	70	1
667	10	111	70	27	0	27.5	0.141	40	1
668	6	98	58	33	190	34.0	0.430	43	0
669	9	154	78	30	100	30.9	0.164	45	0
670	6	165	68	26	168	33.6	0.631	49	0
671	1	99	58	10	0	25.4	0.551	21	0
672	10	68	106	23	49	35.5	0.285	47	0
673	3	123	100	35	240	57.3	0.880	22	0
674	8	91	82	0	0	35.6	0.587	68	0
675	6	195	70	0	0	30.9	0.328	31	1
676	9	156	86	0	0	24.8	0.230	53	1
677	0	93	60	0	0	35.3	0.263	25	0
678	3	121	52	0	0	36.0	0.127	25	1
679	2	101	58	17	265	24.2	0.614	23	0
680	2	56	56	28	45	24.2	0.332	22	0
681	0	162	76	36	0	49.6	0.364	26	1
682	0	95	64	39	105	44.6	0.366	22	0
683	4	125	80	0	0	32.3	0.536	27	1
684	5	136	82	0	0	0.0	0.640	69	0
685	2	129	74	26	205	33.2	0.591	25	0
686	3	130	64	0	0	23.1	0.314	22	0
687	1	107	50	19	0	28.3	0.181	29	0
688	1	140	74	26	180	24.1	0.828	23	0
689	1	144	82	46	180	46.1	0.335	46	1
690	8	107	80	0	0	24.6	0.856	34	0
691	13	158	114	0	0	42.3	0.257	44	1
692	2	121	70	32	95	39.1	0.886	23	0
693	7	129	68	49	125	38.5	0.439	43	1
694	2	90	60	0	0	23.5	0.191	25	0
695	7	142	90	24	480	30.4	0.128	43	1
696	3	169	74	19	125	29.9	0.268	31	1
697	0	99	0	0	0	25.0	0.253	22	0
698	4	127	88	11	155	34.5	0.598	28	0
699	4	118	70	0	0	44.5	0.904	26	0
700	2	122	76	27	200	35.9	0.483	26	0
701	6	125	78	31	0	27.6	0.565	49	1
702	1	168	88	29	0	35.0	0.905	52	1
703	2	129	0	0	0	38.5	0.304	41	0
704	4	110	76	20	100	28.4	0.118	27	0
705	6	80	80	36	0	39.8	0.177	28	0
706	10	115	0	0	0	0.0	0.261	30	1
707	2	127	46	21	335	34.4	0.176	22	0
708	9	164	78	0	0	32.8	0.148	45	1
709	2	93	64	32	160	38.0	0.674	23	1
710	3	158	64	13	387	31.2	0.295	24	0
711	5	126	78	27	22	29.6	0.439	40	0
712	10	129	62	36	0	41.2	0.441	38	1
713	0	134	58	20	291	26.4	0.352	21	0
714	3	102	74	0	0	29.5	0.121	32	0
715	7	187	50	33	392	33.9	0.826	34	1
716	3	173	78	39	185	33.8	0.970	31	1
717	10	94	72	18	0	23.1	0.595	56	0
718	1	108	60	46	178	35.5	0.415	24	0
719	5	97	76	27	0	35.6	0.378	52	1
720	4	83	86	19	0	29.3	0.317	34	0
721	1	114	66	36	200	38.1	0.289	21	0
722	1	149	68	29	127	29.3	0.349	42	1
723	5	117	86	30	105	39.1	0.251	42	0
724	1	111	94	0	0	32.8	0.265	45	0
725	4	112	78	40	0	39.4	0.236	38	0
726	1	116	78	29	180	36.1	0.496	25	0
727	0	141	84	26	0	32.4	0.433	22	0
728	2	175	88	0	0	22.9	0.326	22	0
729	2	92	52	0	0	30.1	0.141	22	0
730	3	130	78	23	79	28.4	0.323	34	1
731	8	120	86	0	0	28.4	0.259	22	1
732	2	174	88	37	120	44.5	0.646	24	1
733	2	106	56	27	165	29.0	0.426	22	0
734	2	105	75	0	0	23.3	0.560	53	0
735	4	95	60	32	0	35.4	0.284	28	0
736	0	126	86	27	120	27.4	0.515	21	0
737	8	65	72	23	0	32.0	0.600	42	0
738	2	99	60	17	160	36.6	0.453	21	0
739	1	102	74	0	0	39.5	0.293	42	1
740	11	120	80	37	150	42.3	0.785	48	1
741	3	102	44	20	94	30.8	0.400	26	0
742	1	109	58	18	116	28.5	0.219	22	0
743	9	140	94	0	0	32.7	0.734	45	1
744	13	153	88	37	140	40.6	1.174	39	0
745	12	100	84	33	105	30.0	0.488	46	0
746	1	147	94	41	0	49.3	0.358	27	1
747	1	81	74	41	57	46.3	1.096	32	0
748	3	187	70	22	200	36.4	0.408	36	1
749	6	162	62	0	0	24.3	0.178	50	1
750	4	136	70	0	0	31.2	1.182	22	1
751	1	121	78	39	74	39.0	0.261	28	0
752	3	108	62	24	0	26.0	0.223	25	0
753	0	181	88	44	510	43.3	0.222	26	1
754	8	154	78	32	0	32.4	0.443	45	1
755	1	128	88	39	110	36.5	1.057	37	1
756	7	137	90	41	0	32.0	0.391	39	0
757	0	123	72	0	0	36.3	0.258	52	1
758	1	106	76	0	0	37.5	0.197	26	0
759	6	190	92	0	0	35.5	0.278	66	1
760	2	88	58	26	16	28.4	0.766	22	0
761	9	170	74	31	0	44.0	0.403	43	1
762	9	89	62	0	0	22.5	0.142	33	0
763	10	101	76	48	180	32.9	0.171	63	0
764	2	122	70	27	0	36.8	0.340	27	0
765	5	121	72	23	112	26.2	0.245	30	0
766	1	126	60	0	0	30.1	0.349	47	1
767	1	93	70	31	0	30.4	0.315	23	0

	Pregnancies	Glucose	BloodPressure	SkinThickness	Insulin	BMI	DiabetesPedigreeFunction	Age	Outcome
0	6	128	69	32	120	28.8	1.527	29	1
1	6	127	68	31	120	27.8	0.527	29	0
2	7	128	68	31	120	27.8	1.527	29	1
3	6	127	68	31	120	28.8	0.527	28	0
4	6	128	68	32	121	28.8	1.527	29	1
5	6	127	69	31	120	27.8	0.527	29	0
6	6	127	68	32	120	28.8	0.527	28	1
7	7	127	68	31	120	28.8	0.527	29	0
8	6	128	69	32	121	28.8	0.527	29	1
9	7	127	69	31	120	27.8	0.527	29	1
10	6	127	69	31	120	28.8	0.527	29	0
11	7	128	69	31	120	28.8	1.527	29	1
12	7	128	69	31	120	27.8	1.527	29	0
13	6	128	68	31	121	28.8	0.527	29	1
14	6	128	69	31	121	27.8	1.527	29	1
15	7	127	68	31	120	28.8	0.527	29	1
16	6	127	69	32	121	28.8	1.527	29	1
17	7	127	69	31	120	28.8	0.527	29	1
18	6	127	68	32	120	28.8	0.527	29	0
19	6	127	69	31	120	28.8	1.527	29	1
20	6	127	69	32	121	28.8	1.527	28	0
21	7	127	69	31	120	28.8	0.527	29	0
22	7	128	69	31	120	28.8	0.527	29	1
23	7	127	69	32	120	28.8	0.527	29	1
24	7	128	69	32	121	28.8	0.527	29	1
25	7	127	69	31	120	28.8	0.527	29	1
26	7	128	69	31	120	28.8	0.527	29	1
27	6	127	68	31	121	27.8	0.527	28	0
28	7	128	69	31	120	27.8	0.527	29	0
29	6	127	69	31	120	28.8	0.527	29	0
30	6	127	69	31	120	28.8	1.527	29	0
31	6	128	69	32	121	28.8	1.527	28	1
32	6	127	68	31	120	27.8	0.527	28	0
33	6	127	69	31	120	27.8	0.527	28	0
34	7	127	69	31	120	27.8	0.527	29	0
35	6	127	68	32	121	27.8	1.527	29	0
36	7	128	69	31	120	28.8	0.527	29	0
37	7	127	69	32	120	28.8	1.527	29	1
38	6	127	68	32	120	28.8	0.527	28	1
39	6	127	69	32	121	28.8	1.527	29	1
40	6	128	68	31	120	28.8	0.527	28	0
41	7	128	69	31	120	28.8	1.527	29	0
42	7	127	69	31	120	27.8	0.527	29	0
43	7	128	69	31	121	28.8	1.527	29	1
44	7	128	68	31	120	27.8	0.527	29	0
45	6	128	68	32	120	28.8	1.527	28	1
46	6	128	68	31	120	28.8	1.527	29	0
47	6	127	69	31	120	28.8	1.527	28	0
48	7	127	68	32	120	28.8	0.527	29	1
49	7	127	68	31	120	27.8	0.527	28	0
50	6	127	69	31	120	27.8	0.527	28	0
51	6	127	68	31	120	27.8	0.527	28	0
52	6	127	68	31	120	27.8	0.527	29	0
53	7	128	69	32	121	28.8	0.527	29	1
54	7	128	68	32	121	28.8	1.527	29	0
55	6	127	68	31	120	27.8	0.527	28	0
56	7	128	68	32	121	28.8	0.527	29	1
57	6	127	69	32	120	28.8	1.527	29	0
58	6	128	69	31	120	28.8	1.527	29	0
59	6	127	68	32	121	28.8	0.527	28	0
60	6	127	68	31	120	27.8	0.527	28	0
61	7	128	69	31	120	28.8	0.527	29	1
62	6	127	68	31	120	27.8	1.527	29	0
63	6	128	68	32	121	27.8	1.527	28	0
64	7	127	68	31	120	28.8	0.527	29	1
65	6	127	69	31	120	28.8	0.527	29	0
66	6	127	69	31	120	28.8	1.527	29	1
67	6	127	69	31	120	28.8	1.527	29	0
68	6	127	68	31	120	27.8	0.527	28	0
69	6	128	69	31	120	28.8	0.527	28	0
70	6	127	68	31	120	28.8	1.527	28	1
71	6	128	68	32	121	28.8	0.527	28	0
72	7	127	69	31	120	28.8	1.527	29	1
73	6	128	69	31	121	28.8	0.527	28	0
74	6	127	69	31	120	28.8	0.527	28	0
75	6	127	68	31	120	27.8	0.527	28	0
76	7	127	69	31	120	28.8	0.527	29	0
77	6	127	69	32	120	28.8	0.527	28	0
78	6	128	68	31	120	28.8	0.527	28	1
79	6	127	68	31	120	27.8	0.527	28	0
80	6	127	68	31	120	27.8	0.527	28	0
81	6	127	68	31	120	27.8	0.527	28	0
82	7	127	69	31	120	28.8	1.527	29	0
83	6	127	68	31	120	27.8	0.527	28	0
84	6	128	69	31	120	28.8	0.527	29	1
85	6	127	69	31	121	28.8	1.527	28	0
86	7	127	69	32	120	28.8	0.527	29	0
87	6	127	68	31	120	28.8	0.527	28	0
88	7	128	69	32	120	28.8	0.527	29	1
89	6	127	68	31	120	27.8	0.527	28	0
90	6	127	68	31	120	27.8	0.527	28	0
91	6	127	69	31	121	28.8	0.527	29	0
92	7	127	69	32	120	28.8	0.527	29	0
93	6	128	69	31	120	27.8	0.527	29	1
94	6	128	69	31	120	27.8	1.527	28	0
95	6	128	69	31	121	28.8	0.527	29	0
96	6	127	68	31	120	28.8	0.527	28	0
97	6	127	68	31	120	27.8	0.527	28	0
98	6	127	68	31	120	28.8	0.527	28	0
99	6	127	69	32	121	28.8	0.527	29	1
100	6	128	69	31	120	28.8	1.527	29	1
101	6	128	68	31	120	27.8	0.527	28	0
102	6	127	69	31	120	27.8	0.527	28	0
103	6	127	69	31	120	27.8	0.527	28	0
104	6	127	68	31	120	28.8	1.527	28	0
105	6	127	68	31	121	28.8	1.527	28	0
106	6	127	69	31	120	27.8	0.527	28	0
107	6	128	68	31	121	28.8	0.527	29	0
108	6	127	68	31	120	28.8	0.527	28	0
109	6	127	69	31	120	28.8	0.527	28	1
110	6	128	69	32	121	28.8	0.527	28	1
111	7	128	68	31	121	28.8	1.527	29	1
112	6	127	69	32	120	28.8	0.527	28	0
113	6	127	68	31	120	28.8	0.527	28	0
114	7	128	68	32	121	28.8	1.527	29	1
115	6	128	69	31	120	28.8	1.527	29	1
116	6	127	69	31	120	28.8	0.527	29	1
117	6	127	68	31	120	28.8	1.527	28	0
118	6	127	68	31	120	28.8	0.527	28	0
119	6	127	69	31	120	27.8	0.527	28	0
120	6	128	69	32	120	28.8	1.527	28	1
121	6	127	68	32	120	28.8	0.527	28	0
122	6	127	69	31	120	28.8	0.527	28	0
123	6	128	69	31	120	27.8	0.527	29	0
124	6	127	69	31	120	28.8	0.527	28	1
125	6	127	68	32	120	28.8	0.527	28	1
126	6	127	69	31	121	28.8	0.527	29	0
127	6	127	68	32	120	28.8	0.527	28	0
128	6	127	69	31	121	28.8	0.527	29	1
129	6	127	69	31	120	28.8	1.527	29	1
130	6	128	69	31	121	28.8	0.527	29	1
131	7	127	68	31	120	28.8	1.527	29	1
132	6	128	68	32	121	28.8	0.527	29	1
133	7	127	69	31	120	28.8	0.527	29	0
134	6	127	68	31	120	27.8	1.527	28	0
135	6	127	68	31	121	28.8	0.527	29	0
136	6	127	69	31	120	28.8	1.527	28	0
137	6	127	68	31	120	28.8	1.527	28	0
138	6	128	69	31	120	28.8	1.527	29	0
139	6	127	69	31	121	28.8	0.527	28	0
140	6	128	69	31	120	27.8	0.527	29	0
141	6	127	69	31	120	28.8	0.527	29	0
142	6	127	68	31	120	28.8	0.527	28	0
143	7	127	68	31	120	28.8	0.527	29	1
144	6	128	68	31	121	28.8	0.527	28	0
145	6	127	69	31	120	27.8	1.527	28	0
146	7	127	69	32	120	28.8	0.527	29	0
147	6	127	68	32	120	28.8	1.527	29	0
148	6	128	69	31	120	28.8	0.527	29	0
149	6	127	69	31	120	27.8	0.527	28	0
150	6	128	69	32	121	28.8	0.527	28	0
151	6	127	68	31	120	27.8	0.527	29	0
152	7	128	69	31	121	28.8	1.527	29	1
153	6	128	69	32	121	28.8	1.527	28	0
154	7	128	69	31	120	28.8	0.527	29	1
155	7	128	69	32	120	28.8	0.527	29	1
156	6	127	68	31	120	27.8	1.527	28	0
157	6	127	68	31	121	27.8	1.527	28	0
158	6	127	69	31	120	28.8	0.527	28	0
159	7	128	69	32	120	28.8	1.527	29	1
160	6	128	69	32	120	28.8	0.527	29	0
161	7	127	69	32	120	28.8	0.527	29	0
162	6	127	69	32	121	28.8	0.527	28	0
163	6	127	68	31	120	28.8	0.527	28	0
164	6	128	69	31	120	28.8	1.527	29	1
165	6	127	69	31	121	28.8	1.527	29	1
166	6	128	68	31	120	28.8	0.527	28	0
167	6	127	68	31	120	28.8	1.527	29	0
168	6	127	68	31	120	28.8	0.527	29	0
169	6	127	69	31	120	28.8	0.527	29	0
170	6	127	69	31	120	28.8	0.527	29	1
171	6	128	69	31	121	28.8	1.527	29	1
172	6	127	68	31	120	28.8	1.527	28	0
173	6	127	68	32	120	28.8	1.527	28	0
174	6	127	68	31	120	28.8	0.527	29	0
175	7	128	69	32	121	28.8	1.527	29	1
176	6	127	69	31	120	28.8	0.527	29	0
177	6	128	69	32	121	28.8	0.527	28	1
178	6	128	69	31	120	28.8	0.527	29	0
179	6	128	69	31	120	28.8	1.527	29	1
180	6	127	69	31	120	27.8	0.527	29	0
181	6	127	68	31	120	28.8	1.527	28	0
182	6	127	69	31	120	27.8	0.527	28	0
183	6	127	68	31	120	27.8	0.527	28	0
184	6	128	69	31	120	27.8	0.527	29	0
185	7	128	68	31	120	28.8	1.527	29	1
186	7	128	68	32	121	28.8	1.527	29	1
187	6	128	69	32	120	28.8	1.527	29	1
188	7	127	69	32	120	28.8	1.527	29	1
189	6	128	69	32	121	28.8	0.527	28	1
190	6	127	68	31	120	27.8	0.527	28	0
191	7	127	69	32	120	28.8	0.527	29	0
192	7	128	68	31	120	28.8	0.527	29	1
193	7	128	68	31	120	28.8	1.527	29	1
194	7	127	68	31	120	27.8	0.527	29	0
195	6	128	69	32	121	28.8	0.527	29	1
196	6	127	68	31	120	27.8	0.527	28	0
197	6	127	68	31	120	27.8	1.527	28	1
198	6	127	68	32	120	28.8	1.527	28	1
199	6	128	68	31	121	28.8	0.527	29	1
200	6	127	69	31	120	28.8	1.527	28	0
201	6	128	69	31	120	28.8	0.527	28	0
202	6	127	68	31	120	27.8	1.527	29	0
203	6	127	69	31	120	27.8	0.527	28	0
204	6	127	69	32	121	28.8	0.527	29	0
205	6	127	69	31	120	27.8	0.527	28	0
206	7	128	69	31	121	28.8	1.527	29	1
207	6	128	69	31	120	28.8	0.527	29	1
208	6	127	68	31	120	28.8	0.527	28	0
209	7	128	69	32	120	28.8	0.527	29	1
210	6	127	68	31	120	27.8	0.527	28	0
211	6	128	69	32	120	28.8	0.527	28	0
212	7	128	69	31	120	28.8	0.527	29	0
213	6	128	68	31	121	28.8	0.527	28	1
214	7	127	69	32	121	28.8	0.527	29	1
215	7	128	69	32	121	28.8	1.527	29	1
216	6	127	68	32	121	28.8	0.527	28	1
217	6	127	68	31	120	28.8	0.527	29	0
218	6	127	69	31	120	28.8	1.527	29	1
219	6	127	68	31	120	28.8	0.527	29	1
220	6	128	68	31	121	28.8	1.527	28	1
221	6	128	69	31	120	28.8	1.527	29	1
222	7	127	68	31	120	27.8	0.527	29	0
223	7	128	68	32	121	28.8	1.527	29	0
224	6	127	68	31	120	27.8	1.527	28	0
225	6	127	69	31	120	28.8	0.527	28	0
226	6	127	69	31	120	28.8	0.527	28	0
227	6	128	68	32	120	28.8	1.527	28	1
228	6	128	69	32	121	28.8	1.527	29	0
229	6	127	69	31	120	28.8	0.527	28	0
230	6	128	69	31	120	28.8	1.527	28	1
231	6	128	69	32	121	28.8	0.527	29	1
232	6	127	69	31	120	27.8	1.527	28	0
233	6	127	68	31	120	28.8	0.527	29	0
234	6	127	68	31	120	28.8	0.527	28	0
235	6	128	69	31	120	28.8	0.527	28	1
236	7	128	69	31	121	28.8	1.527	29	1
237	6	128	69	31	120	28.8	1.527	28	1
238	7	128	69	31	120	28.8	1.527	29	1
239	6	127	69	31	120	27.8	1.527	28	0
240	6	127	68	31	120	28.8	0.527	28	0
241	6	127	69	32	120	28.8	0.527	28	0
242	6	128	68	31	120	27.8	0.527	28	1
243	6	127	68	31	121	27.8	1.527	29	1
244	6	128	69	32	121	28.8	0.527	29	0
245	7	128	69	31	120	28.8	1.527	29	1
246	7	127	68	31	120	28.8	0.527	29	0
247	6	128	69	32	121	28.8	0.527	28	0
248	7	127	69	32	121	28.8	0.527	29	0
249	6	127	69	31	120	28.8	0.527	28	0
250	7	127	68	31	120	28.8	0.527	29	0
251	6	128	69	31	120	28.8	0.527	28	0
252	6	127	69	31	120	27.8	0.527	28	0
253	6	127	68	32	120	28.8	0.527	28	0
254	7	127	68	31	121	27.8	1.527	29	1
255	6	127	68	32	120	28.8	1.527	28	1
256	6	127	68	32	120	28.8	1.527	29	0
257	6	127	68	31	120	28.8	0.527	28	0
258	6	128	68	31	121	27.8	1.527	28	0
259	7	128	69	31	121	28.8	1.527	29	1
260	6	128	68	31	121	28.8	0.527	29	0
261	6	128	68	31	120	28.8	1.527	28	1
262	6	127	69	32	120	28.8	1.527	28	0
263	6	128	69	31	120	28.8	0.527	29	0
264	6	127	68	31	120	28.8	0.527	29	1
265	6	127	69	31	120	28.8	1.527	29	0
266	6	128	68	31	120	28.8	1.527	28	1
267	6	128	68	32	120	28.8	1.527	28	0
268	6	127	68	31	120	27.8	0.527	28	0
269	6	128	68	31	120	27.8	0.527	28	1
270	7	127	69	32	120	28.8	1.527	29	1
271	6	127	68	32	120	27.8	0.527	28	0
272	6	127	69	31	120	27.8	0.527	29	0
273	6	127	69	32	120	28.8	0.527	28	0
274	7	127	69	31	120	28.8	0.527	29	0
275	6	127	69	32	120	28.8	1.527	28	0
276	7	127	68	31	120	27.8	0.527	29	1
277	6	127	68	31	120	27.8	0.527	28	0
278	6	127	69	31	120	27.8	1.527	29	0
279	6	127	68	31	121	27.8	1.527	28	0
280	6	128	69	31	120	28.8	0.527	28	1
281	7	128	69	31	121	28.8	0.527	29	0
282	7	128	69	31	121	28.8	0.527	29	0
283	7	128	69	31	120	28.8	0.527	29	1
284	6	127	69	31	120	27.8	0.527	29	1
285	7	128	69	31	121	27.8	1.527	29	0
286	6	128	69	32	121	28.8	1.527	29	0
287	6	127	69	32	121	28.8	1.527	29	1
288	6	127	68	31	120	27.8	0.527	28	0
289	6	127	69	32	120	28.8	0.527	29	0
290	6	127	69	31	120	28.8	0.527	28	0
291	6	127	68	31	120	28.8	1.527	28	1
292	6	128	69	32	121	28.8	1.527	29	1
293	6	128	68	32	121	28.8	1.527	28	1
294	6	128	68	31	120	27.8	0.527	29	0
295	6	128	68	31	120	28.8	1.527	28	0
296	6	128	69	32	121	28.8	0.527	29	1
297	6	127	69	31	121	28.8	0.527	28	0
298	7	127	69	31	121	28.8	0.527	29	1
299	7	127	69	31	120	27.8	1.527	29	0
300	6	128	68	31	120	28.8	1.527	29	1
301	6	128	68	32	121	28.8	0.527	28	1
302	6	127	69	32	120	28.8	0.527	29	0
303	6	127	69	31	120	28.8	0.527	28	1
304	6	128	69	31	120	27.8	0.527	29	0
305	6	127	69	32	120	28.8	0.527	29	0
306	7	128	68	31	121	27.8	0.527	29	1
307	6	128	68	31	121	27.8	0.527	28	0
308	6	128	68	31	121	28.8	1.527	28	1
309	6	127	68	31	121	28.8	1.527	29	1
310	6	127	68	31	120	27.8	0.527	29	0
311	6	127	69	32	121	28.8	1.527	28	0
312	6	128	69	31	120	27.8	0.527	28	1
313	6	127	68	31	120	28.8	1.527	28	0
314	7	127	69	31	120	28.8	1.527	29	1
315	6	127	68	31	120	28.8	0.527	28	0
316	6	127	69	31	120	27.8	0.527	29	0
317	6	128	69	31	120	28.8	0.527	29	1
318	6	127	68	32	121	28.8	0.527	28	0
319	6	128	69	31	120	27.8	0.527	29	1
320	6	128	68	31	121	27.8	0.527	29	0
321	6	127	69	31	120	28.8	0.527	28	1
322	6	127	69	31	120	27.8	0.527	29	1
323	7	128	69	32	120	27.8	1.527	29	1
324	6	127	69	32	120	28.8	0.527	28	0
325	6	128	69	31	121	27.8	0.527	28	0
326	6	127	68	32	121	28.8	1.527	29	1
327	7	128	69	31	120	28.8	0.527	29	0
328	6	127	69	32	120	28.8	0.527	28	1
329	6	127	69	32	120	28.8	0.527	29	0
330	7	127	69	31	120	27.8	1.527	29	0
331	6	127	68	31	120	28.8	0.527	28	0
332	6	128	68	31	120	28.8	0.527	29	1
333	7	127	69	31	120	27.8	0.527	29	0
334	6	127	68	31	120	27.8	0.527	28	0
335	6	128	69	32	121	28.8	0.527	28	0
336	6	127	68	31	120	28.8	1.527	29	0
337	6	127	69	31	120	28.8	0.527	29	1
338	7	128	69	32	121	28.8	1.527	29	1
339	7	128	69	31	120	28.8	0.527	29	1
340	6	128	69	31	120	27.8	0.527	28	0
341	6	127	69	31	120	27.8	1.527	29	0
342	6	127	68	32	120	28.8	0.527	28	0
343	6	127	69	31	120	28.8	0.527	29	0
344	7	127	69	31	120	28.8	0.527	29	0
345	7	127	69	32	120	28.8	0.527	29	0
346	6	128	68	31	120	28.8	1.527	28	0
347	6	127	68	31	120	27.8	0.527	28	0
348	6	127	68	31	120	27.8	0.527	28	0
349	6	127	69	32	120	28.8	0.527	29	1
350	6	127	69	31	120	28.8	0.527	29	0
351	6	128	69	31	120	28.8	0.527	29	0
352	6	127	69	31	120	28.8	0.527	29	0
353	6	127	68	31	120	27.8	1.527	28	0
354	6	127	69	31	120	28.8	1.527	28	0
355	7	128	69	31	120	28.8	0.527	29	1
356	6	127	68	32	121	28.8	1.527	28	1
357	7	128	68	31	120	28.8	1.527	29	1
358	7	127	69	32	120	28.8	0.527	29	0
359	6	128	69	32	121	28.8	1.527	29	1
360	6	128	68	32	121	28.8	1.527	29	1
361	6	128	69	31	120	28.8	0.527	29	0
362	6	127	69	32	120	28.8	0.527	29	0
363	6	128	69	31	120	28.8	0.527	29	1
364	6	128	69	31	121	28.8	0.527	29	0
365	6	127	68	31	120	28.8	0.527	29	0
366	6	127	69	31	120	27.8	0.527	29	1
367	6	127	68	31	120	27.8	0.527	28	0
368	6	127	69	31	120	27.8	0.527	28	0
369	6	128	69	31	121	28.8	0.527	29	1
370	6	128	69	32	121	28.8	1.527	28	1
371	6	127	68	31	120	27.8	1.527	28	0
372	6	127	68	31	120	28.8	1.527	28	0
373	6	127	68	32	120	28.8	0.527	28	0
374	6	127	68	32	121	28.8	1.527	28	0
375	7	128	69	32	121	28.8	1.527	29	1
376	6	127	69	31	120	27.8	0.527	28	0
377	6	127	68	32	120	28.8	0.527	28	0
378	6	128	69	31	120	28.8	0.527	29	1
379	6	127	69	32	120	28.8	1.527	29	0
380	6	127	69	31	120	28.8	1.527	28	0
381	6	127	68	31	120	27.8	0.527	28	0
382	6	127	68	31	121	27.8	1.527	28	0
383	6	127	68	31	120	27.8	1.527	28	0
384	6	127	69	31	120	27.8	0.527	28	0
385	6	127	68	31	120	27.8	0.527	28	0
386	6	127	69	31	120	28.8	1.527	29	1
387	7	127	69	32	120	28.8	0.527	29	1
388	6	128	69	31	121	28.8	0.527	29	1
389	6	127	68	31	120	28.8	1.527	28	0
390	6	127	68	31	121	28.8	0.527	29	0
391	6	128	69	31	120	28.8	0.527	28	1
392	6	128	68	31	121	27.8	0.527	28	0
393	6	127	69	31	120	27.8	0.527	29	0
394	6	128	69	31	120	28.8	1.527	29	1
395	6	127	68	31	121	27.8	1.527	28	0
396	6	127	68	32	120	27.8	1.527	29	0
397	6	128	68	32	120	28.8	0.527	28	1
398	6	127	69	31	120	27.8	0.527	28	0
399	6	128	69	31	120	28.8	0.527	28	1
400	6	127	68	31	120	28.8	0.527	29	1
401	6	128	68	31	120	27.8	0.527	29	0
402	6	128	69	32	120	28.8	0.527	29	1
403	7	127	69	31	120	28.8	0.527	29	0
404	6	128	68	31	120	28.8	0.527	29	1
405	6	127	68	32	121	28.8	0.527	28	0
406	6	127	69	31	120	28.8	0.527	29	1
407	6	127	68	31	120	27.8	0.527	28	0
408	7	128	69	31	120	27.8	1.527	29	1
409	6	128	68	32	121	28.8	1.527	28	1
410	6	127	69	32	120	28.8	1.527	28	0
411	6	127	69	31	121	28.8	1.527	28	0
412	6	128	69	31	121	28.8	1.527	28	0
413	6	128	69	31	120	27.8	0.527	28	0
414	6	128	68	32	121	28.8	1.527	28	1
415	6	128	69	32	121	28.8	0.527	28	1
416	6	127	68	31	120	27.8	1.527	28	0
417	6	128	69	32	120	28.8	1.527	29	1
418	6	127	68	31	120	27.8	1.527	28	0
419	6	128	68	31	120	27.8	0.527	28	1
420	6	127	69	32	121	28.8	0.527	28	0
421	6	127	68	31	120	27.8	1.527	28	0
422	6	127	68	32	120	28.8	0.527	28	0
423	6	127	68	31	120	28.8	0.527	28	0
424	7	128	69	32	121	28.8	0.527	29	1
425	6	128	69	32	121	28.8	0.527	29	1
426	6	127	68	31	120	27.8	0.527	28	0
427	6	128	68	31	121	28.8	0.527	29	1
428	6	128	69	32	121	28.8	0.527	28	0
429	6	127	69	31	121	28.8	0.527	29	1
430	6	127	68	31	120	27.8	0.527	28	0
431	6	127	69	31	120	28.8	1.527	29	0
432	6	127	69	31	120	28.8	0.527	28	0
433	6	128	69	31	120	27.8	0.527	29	0
434	6	127	68	31	120	27.8	1.527	29	0
435	6	128	68	31	120	28.8	0.527	29	1
436	7	128	69	32	120	28.8	0.527	29	0
437	6	128	69	31	120	28.8	0.527	28	0
438	6	127	69	31	120	27.8	0.527	28	0
439	6	127	69	31	120	28.8	1.527	29	0
440	6	128	69	31	120	28.8	0.527	29	1
441	6	127	68	31	120	28.8	0.527	28	0
442	6	127	68	31	120	28.8	0.527	28	0
443	7	127	69	31	120	28.8	1.527	29	1
444	6	127	68	31	120	28.8	0.527	29	1
445	6	128	69	32	120	28.8	1.527	28	1
446	6	127	69	31	120	27.8	1.527	28	0
447	6	127	69	32	120	28.8	0.527	28	0
448	6	127	68	32	120	28.8	0.527	28	1
449	6	127	69	31	120	28.8	0.527	28	0
450	6	127	68	31	120	27.8	0.527	28	0
451	6	128	69	31	120	28.8	1.527	28	1
452	6	127	68	32	121	28.8	0.527	28	0
453	6	127	68	31	120	27.8	1.527	29	0
454	6	127	68	31	120	28.8	0.527	28	0
455	7	128	68	31	120	28.8	0.527	29	1
456	6	128	68	31	120	27.8	1.527	29	0
457	6	127	68	31	120	28.8	0.527	28	0
458	7	128	69	32	121	28.8	1.527	29	1
459	7	128	69	32	120	27.8	0.527	29	0
460	7	127	69	31	120	27.8	1.527	29	0
461	6	127	68	31	120	27.8	0.527	28	0
462	7	127	69	32	120	28.8	1.527	29	0
463	6	127	69	31	120	27.8	0.527	29	0
464	7	127	69	31	120	27.8	1.527	29	0
465	6	127	68	31	120	27.8	0.527	28	0
466	6	127	68	31	120	27.8	0.527	28	0
467	6	127	68	32	120	28.8	1.527	28	0
468	7	127	68	31	120	28.8	0.527	29	1
469	6	128	69	32	121	28.8	1.527	28	0
470	6	128	69	32	120	28.8	1.527	28	0
471	6	128	69	32	120	28.8	0.527	28	0
472	6	127	68	31	120	28.8	0.527	28	0
473	7	128	69	31	120	28.8	0.527	29	0
474	6	127	68	31	120	28.8	0.527	28	0
475	6	128	69	31	120	27.8	0.527	29	0
476	6	127	69	32	121	28.8	1.527	29	1
477	7	127	69	31	120	27.8	0.527	29	0
478	7	127	69	32	120	27.8	0.527	29	0
479	6	128	69	31	120	28.8	0.527	29	0
480	6	128	69	31	121	28.8	0.527	29	1
481	6	127	69	32	120	28.8	0.527	29	0
482	6	127	68	31	120	27.8	0.527	28	0
483	6	127	69	31	121	28.8	0.527	28	0
484	6	128	68	31	120	28.8	1.527	29	1
485	6	128	68	32	121	28.8	0.527	28	1
486	6	128	68	32	121	28.8	1.527	28	0
487	6	128	69	32	121	28.8	1.527	29	0
488	6	127	69	31	120	27.8	0.527	28	0
489	7	128	69	31	120	27.8	1.527	29	0
490	6	127	68	31	120	28.8	1.527	28	0
491	6	127	69	31	120	28.8	0.527	29	0
492	6	127	68	32	120	28.8	0.527	29	0
493	6	127	69	31	121	28.8	1.527	29	1
494	6	127	68	31	120	27.8	0.527	28	0
495	6	128	69	31	120	27.8	0.527	29	0
496	6	127	68	31	120	27.8	0.527	29	0
497	6	127	69	31	120	28.8	1.527	28	0
498	7	128	69	32	121	27.8	0.527	29	1
499	6	128	69	32	121	28.8	1.527	29	0
500	6	127	69	31	120	27.8	0.527	28	0
501	6	127	69	32	120	28.8	0.527	28	0
502	6	127	68	32	120	28.8	1.527	29	1
503	7	127	68	31	120	28.8	1.527	29	0
504	6	127	69	32	120	28.8	0.527	29	0
505	7	127	69	31	120	28.8	0.527	29	0
506	6	128	69	31	120	28.8	0.527	29	1
507	6	128	68	31	121	28.8	1.527	28	0
508	6	127	68	31	120	28.8	1.527	28	0
509	7	127	69	31	120	27.8	0.527	29	0
510	7	127	69	31	120	28.8	0.527	29	1
511	6	128	68	31	121	27.8	0.527	28	0
512	7	127	68	31	120	27.8	0.527	29	0
513	6	127	68	31	120	27.8	0.527	28	0
514	6	127	68	31	120	27.8	0.527	28	0
515	6	128	69	31	120	28.8	0.527	28	1
516	7	128	69	32	121	28.8	1.527	29	1
517	7	127	69	31	120	28.8	0.527	29	0
518	7	127	68	31	120	28.8	0.527	29	0
519	6	128	69	31	121	27.8	1.527	29	0
520	6	127	69	32	120	27.8	0.527	28	0
521	6	127	69	32	121	28.8	0.527	28	0
522	6	127	68	31	120	27.8	0.527	28	0
523	7	128	69	31	120	28.8	1.527	29	1
524	6	127	68	31	120	28.8	0.527	28	0
525	6	127	68	31	120	27.8	0.527	28	0
526	6	127	68	31	120	27.8	0.527	28	0
527	6	127	69	31	120	27.8	0.527	28	0
528	6	127	68	31	121	28.8	0.527	28	0
529	6	127	68	31	120	27.8	1.527	29	0
530	6	127	68	31	120	28.8	1.527	28	0
531	6	127	69	31	120	28.8	1.527	28	0
532	6	127	68	32	120	28.8	1.527	29	0
533	6	127	68	31	120	28.8	0.527	29	0
534	6	127	68	31	120	28.8	1.527	28	0
535	6	128	68	31	120	28.8	0.527	28	1
536	6	127	69	31	120	28.8	0.527	29	0
537	6	127	68	31	120	27.8	1.527	29	0
538	6	127	69	32	121	28.8	1.527	28	0
539	6	128	69	32	121	28.8	1.527	29	1
540	7	127	69	32	121	28.8	1.527	29	1
541	6	128	69	31	121	28.8	1.527	28	1
542	7	127	69	32	120	28.8	1.527	29	1
543	6	127	69	31	120	28.8	0.527	28	0
544	6	127	69	31	120	28.8	0.527	29	0
545	7	128	69	32	121	28.8	0.527	29	1
546	6	128	69	31	121	28.8	1.527	29	1
547	6	128	68	31	121	28.8	0.527	28	0
548	6	128	69	32	120	28.8	0.527	29	0
549	6	128	69	31	120	28.8	1.527	29	0
550	6	127	69	31	120	27.8	0.527	28	0
551	6	127	68	31	120	28.8	1.527	28	0
552	6	127	69	31	120	27.8	0.527	29	0
553	6	127	68	31	120	28.8	0.527	28	0
554	6	127	68	31	120	28.8	0.527	28	0
555	7	127	69	32	121	27.8	0.527	29	0
556	6	127	69	32	120	28.8	0.527	29	0
557	7	127	69	31	120	27.8	0.527	29	0
558	7	127	68	32	120	28.8	0.527	29	0
559	7	127	69	31	120	28.8	0.527	29	0
560	6	127	69	31	120	28.8	0.527	29	1
561	6	128	68	32	121	28.8	0.527	28	1
562	6	127	68	32	120	28.8	0.527	28	0
563	6	127	68	31	120	27.8	0.527	29	0
564	6	127	69	31	120	28.8	1.527	28	0
565	6	127	68	31	120	27.8	1.527	28	0
566	6	127	69	31	120	28.8	0.527	28	0
567	6	127	68	32	121	28.8	0.527	29	0
568	6	128	69	31	121	28.8	0.527	29	0
569	6	127	68	31	121	28.8	0.527	29	1
570	6	127	69	31	120	28.8	0.527	29	0
571	6	128	69	31	120	27.8	0.527	28	0
572	6	127	68	31	120	28.8	0.527	28	0
573	6	127	68	31	120	28.8	0.527	28	0
574	6	128	69	31	121	28.8	1.527	28	0
575	6	127	68	32	120	28.8	0.527	28	0
576	6	127	68	31	121	27.8	1.527	29	0
577	6	127	69	31	120	28.8	1.527	28	1
578	7	128	68	31	120	27.8	0.527	29	0
579	6	128	69	32	120	28.8	1.527	29	1
580	6	128	69	32	120	28.8	0.527	28	1
581	6	127	68	31	120	27.8	0.527	28	0
582	7	127	69	31	120	27.8	0.527	29	0
583	7	127	69	31	120	28.8	0.527	29	0
584	7	127	69	31	121	28.8	1.527	29	1
585	6	127	68	31	120	27.8	0.527	28	0
586	7	128	68	31	120	28.8	0.527	29	1
587	6	127	68	31	120	27.8	0.527	29	0
588	6	128	69	31	121	28.8	1.527	29	1
589	6	127	68	31	120	27.8	0.527	28	0
590	7	127	69	32	120	28.8	1.527	29	1
591	6	127	69	32	121	28.8	0.527	28	0
592	6	128	69	31	120	28.8	0.527	29	1
593	6	127	68	31	120	28.8	1.527	28	0
594	6	127	69	32	121	28.8	1.527	29	0
595	6	128	69	31	121	28.8	1.527	28	1
596	6	127	69	31	120	28.8	0.527	29	0
597	6	127	68	31	120	27.8	1.527	28	0
598	6	128	69	31	120	28.8	0.527	29	1
599	6	127	68	31	120	27.8	0.527	28	0
600	6	127	69	31	120	27.8	0.527	28	0
601	6	127	68	31	120	27.8	0.527	28	0
602	6	127	69	32	120	27.8	0.527	29	0
603	7	128	69	31	121	28.8	1.527	29	1
604	6	128	68	31	120	28.8	0.527	29	1
605	6	127	68	32	120	28.8	0.527	28	0
606	6	128	69	32	121	28.8	1.527	28	1
607	6	127	68	31	120	27.8	0.527	28	0
608	6	128	69	32	121	28.8	0.527	28	0
609	6	127	68	31	121	27.8	0.527	28	0
610	6	127	68	31	121	28.8	0.527	28	0
611	6	128	68	31	121	28.8	1.527	29	1
612	7	128	69	32	121	28.8	1.527	29	1
613	6	127	69	31	120	28.8	1.527	28	0
614	7	128	69	31	121	28.8	1.527	29	1
615	6	127	69	31	120	27.8	0.527	28	0
616	6	127	69	31	120	28.8	0.527	29	0
617	6	127	68	31	120	27.8	0.527	28	0
618	7	127	69	31	120	28.8	1.527	29	1
619	6	127	68	31	120	28.8	0.527	28	1
620	6	127	69	32	121	28.8	0.527	28	0
621	6	127	69	31	120	27.8	1.527	28	0
622	6	128	69	31	120	28.8	1.527	29	0
623	6	127	69	31	120	28.8	0.527	28	0
624	6	127	68	31	120	28.8	0.527	28	0
625	6	127	69	32	120	28.8	0.527	29	0
626	6	127	68	31	120	27.8	0.527	28	0
627	6	128	69	31	120	28.8	0.527	28	0
628	6	128	69	31	120	28.8	0.527	29	0
629	6	127	68	31	120	27.8	0.527	28	0
630	7	127	68	31	120	27.8	1.527	29	1
631	6	127	69	32	120	28.8	0.527	28	0
632	6	127	68	31	120	27.8	0.527	28	0
633	6	128	69	31	121	27.8	0.527	28	0
634	7	127	68	31	120	27.8	0.527	29	0
635	7	127	69	31	120	28.8	0.527	29	1
636	6	127	69	31	120	28.8	0.527	29	0
637	6	127	69	31	120	28.8	1.527	28	0
638	7	127	69	32	120	28.8	1.527	29	1
639	6	127	69	31	120	27.8	0.527	28	0
640	6	127	69	31	120	28.8	1.527	28	0
641	6	128	69	31	120	28.8	0.527	28	0
642	6	128	69	31	120	28.8	0.527	29	1
643	6	127	68	31	120	28.8	1.527	29	0
644	6	127	69	31	121	27.8	1.527	28	0
645	6	128	69	32	121	28.8	0.527	29	0
646	6	128	69	31	121	27.8	0.527	29	1
647	6	128	68	32	121	28.8	0.527	28	1
648	7	128	69	32	121	28.8	0.527	29	1
649	6	127	68	31	120	27.8	0.527	28	0
650	6	127	68	31	120	27.8	0.527	28	0
651	6	127	68	31	120	28.8	0.527	28	0
652	6	127	69	32	120	28.8	0.527	28	0
653	6	127	68	31	120	27.8	0.527	28	0
654	6	127	69	31	121	28.8	0.527	28	0
655	6	128	68	31	121	28.8	0.527	28	1
656	6	127	68	32	120	27.8	0.527	28	0
657	6	127	69	32	121	28.8	1.527	29	0
658	7	127	69	31	120	28.8	0.527	29	0
659	6	127	69	31	120	28.8	1.527	28	1
660	7	128	69	31	120	27.8	0.527	29	0
661	6	128	69	32	120	28.8	1.527	28	1
662	7	128	69	32	121	28.8	0.527	29	1
663	7	128	69	32	121	28.8	1.527	29	1
664	6	127	68	32	120	28.8	0.527	29	1
665	6	127	69	32	121	28.8	0.527	28	0
666	6	128	69	31	120	28.8	0.527	29	1
667	7	127	69	31	120	27.8	0.527	29	1
668	6	127	68	32	121	28.8	0.527	29	0
669	7	128	69	31	120	28.8	0.527	29	0
670	6	128	68	31	121	28.8	1.527	29	0
671	6	127	68	31	120	27.8	1.527	28	0
672	7	127	69	31	120	28.8	0.527	29	0
673	6	127	69	32	121	28.8	1.527	28	0
674	7	127	69	31	120	28.8	1.527	29	0
675	6	128	69	31	120	28.8	0.527	29	1
676	7	128	69	31	120	27.8	0.527	29	1
677	6	127	68	31	120	28.8	0.527	28	0
678	6	127	68	31	120	28.8	0.527	28	1
679	6	127	68	31	121	27.8	1.527	28	0
680	6	127	68	31	120	27.8	0.527	28	0
681	6	128	69	32	120	28.8	0.527	28	1
682	6	127	68	32	120	28.8	0.527	28	0
683	6	127	69	31	120	28.8	1.527	28	1
684	6	128	69	31	120	27.8	1.527	29	0
685	6	128	69	31	121	28.8	1.527	28	0
686	6	128	68	31	120	27.8	0.527	28	0
687	6	127	68	31	120	28.8	0.527	29	0
688	6	128	69	31	121	27.8	1.527	28	0
689	6	128	69	32	121	28.8	0.527	29	1
690	7	127	69	31	120	27.8	1.527	29	0
691	7	128	69	31	120	28.8	0.527	29	1
692	6	127	69	32	120	28.8	1.527	28	0
693	7	128	68	32	121	28.8	0.527	29	1
694	6	127	68	31	120	27.8	0.527	28	0
695	7	128	69	31	121	28.8	0.527	29	1
696	6	128	69	31	121	28.8	0.527	29	1
697	6	127	68	31	120	27.8	0.527	28	0
698	6	127	69	31	121	28.8	1.527	28	0
699	6	127	69	31	120	28.8	1.527	28	0
700	6	127	69	31	121	28.8	0.527	28	0
701	6	127	69	31	120	27.8	1.527	29	1
702	6	128	69	31	120	28.8	1.527	29	1
703	6	128	68	31	120	28.8	0.527	29	0
704	6	127	69	31	120	28.8	0.527	28	0
705	6	127	69	32	120	28.8	0.527	28	0
706	7	127	68	31	120	27.8	0.527	29	1
707	6	127	68	31	121	28.8	0.527	28	0
708	7	128	69	31	120	28.8	0.527	29	1
709	6	127	68	32	121	28.8	1.527	28	1
710	6	128	68	31	121	28.8	0.527	28	0
711	6	127	69	31	120	28.8	0.527	29	0
712	7	128	68	32	120	28.8	0.527	29	1
713	6	128	68	31	121	27.8	0.527	28	0
714	6	127	69	31	120	28.8	0.527	29	0
715	7	128	68	32	121	28.8	1.527	29	1
716	6	128	69	32	121	28.8	1.527	29	1
717	7	127	69	31	120	27.8	1.527	29	0
718	6	127	68	32	121	28.8	0.527	28	0
719	6	127	69	31	120	28.8	0.527	29	1
720	6	127	69	31	120	28.8	0.527	29	0
721	6	127	68	32	121	28.8	0.527	28	0
722	6	128	68	31	121	28.8	0.527	29	1
723	6	127	69	31	120	28.8	0.527	29	0
724	6	127	69	31	120	28.8	0.527	29	0
725	6	127	69	32	120	28.8	0.527	29	0
726	6	127	69	31	121	28.8	0.527	28	0
727	6	128	69	31	120	28.8	0.527	28	0
728	6	128	69	31	120	27.8	0.527	28	0
729	6	127	68	31	120	28.8	0.527	28	0
730	6	128	69	31	120	28.8	0.527	29	1
731	7	127	69	31	120	28.8	0.527	28	1
732	6	128	69	32	120	28.8	1.527	28	1
733	6	127	68	31	121	28.8	0.527	28	0
734	6	127	69	31	120	27.8	1.527	29	0
735	6	127	68	32	120	28.8	0.527	28	0
736	6	127	69	31	120	27.8	0.527	28	0
737	7	127	69	31	120	28.8	1.527	29	0
738	6	127	68	31	121	28.8	0.527	28	0
739	6	127	69	31	120	28.8	0.527	29	1
740	7	127	69	32	121	28.8	1.527	29	1
741	6	127	68	31	120	28.8	0.527	28	0
742	6	127	68	31	120	28.8	0.527	28	0
743	7	128	69	31	120	28.8	1.527	29	1
744	7	128	69	32	121	28.8	1.527	29	0
745	7	127	69	32	120	28.8	0.527	29	0
746	6	128	69	32	120	28.8	0.527	28	1
747	6	127	69	32	120	28.8	1.527	29	0
748	6	128	69	31	121	28.8	0.527	29	1
749	6	128	68	31	120	27.8	0.527	29	1
750	6	128	69	31	120	28.8	1.527	28	1
751	6	127	69	32	120	28.8	0.527	28	0
752	6	127	68	31	120	27.8	0.527	28	0
753	6	128	69	32	121	28.8	0.527	28	1
754	7	128	69	32	120	28.8	0.527	29	1
755	6	128	69	32	120	28.8	1.527	29	1
756	7	128	69	32	120	28.8	0.527	29	0
757	6	127	69	31	120	28.8	0.527	29	1
758	6	127	69	31	120	28.8	0.527	28	0
759	6	128	69	31	120	28.8	0.527	29	1
760	6	127	68	31	120	28.8	1.527	28	0
761	7	128	69	31	120	28.8	0.527	29	1
762	7	127	68	31	120	27.8	0.527	29	0
763	7	127	69	32	121	28.8	0.527	29	0
764	6	127	69	31	120	28.8	0.527	28	0
765	6	127	69	31	120	27.8	0.527	29	0
766	6	127	68	31	120	28.8	0.527	29	1
767	6	127	69	31	120	28.8	0.527	28	0