Ensemble Tutorial

Ensemble Learning: Bagging Tutorial: Heart disease prediction

dataset credits: https://www.kaggle.com/fedesoriano/heart-failure-prediction

Data Loading 

import pandas as pd

df = pd.read_csv("Data/heart.csv")
df.head()
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
0 40 M ATA 140 289 0 Normal 172 N 0.0 Up 0
1 49 F NAP 160 180 0 Normal 156 N 1.0 Flat 1
2 37 M ATA 130 283 0 ST 98 N 0.0 Up 0
3 48 F ASY 138 214 0 Normal 108 Y 1.5 Flat 1
4 54 M NAP 150 195 0 Normal 122 N 0.0 Up 0 
df.shape
(918, 12)
df.describe()
Age RestingBP Cholesterol FastingBS MaxHR Oldpeak HeartDisease
count 918.000000 918.000000 918.000000 918.000000 918.000000 918.000000 918.000000
mean 53.510893 132.396514 198.799564 0.233115 136.809368 0.887364 0.553377
std 9.432617 18.514154 109.384145 0.423046 25.460334 1.066570 0.497414
min 28.000000 0.000000 0.000000 0.000000 60.000000 -2.600000 0.000000
25% 47.000000 120.000000 173.250000 0.000000 120.000000 0.000000 0.000000
50% 54.000000 130.000000 223.000000 0.000000 138.000000 0.600000 1.000000
75% 60.000000 140.000000 267.000000 0.000000 156.000000 1.500000 1.000000
max 77.000000 200.000000 603.000000 1.000000 202.000000 6.200000 1.000000

Treat Outliers 

df[df.Cholesterol>(df.Cholesterol.mean()+3*df.Cholesterol.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
76 32 M ASY 118 529 0 Normal 130 N 0.0 Flat 1
149 54 M ASY 130 603 1 Normal 125 Y 1.0 Flat 1
616 67 F NAP 115 564 0 LVH 160 N 1.6 Flat 0 
df.shape
(918, 12)
df1 = df[df.Cholesterol<=(df.Cholesterol.mean()+3*df.Cholesterol.std())]
df1.shape
(915, 12)
df[df.MaxHR>(df.MaxHR.mean()+3*df.MaxHR.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease 
df[df.FastingBS>(df.FastingBS.mean()+3*df.FastingBS.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease 
df[df.Oldpeak>(df.Oldpeak.mean()+3*df.Oldpeak.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
166 50 M ASY 140 231 0 ST 140 Y 5.0 Flat 1
702 59 M TA 178 270 0 LVH 145 N 4.2 Down 0
771 55 M ASY 140 217 0 Normal 111 Y 5.6 Down 1
791 51 M ASY 140 298 0 Normal 122 Y 4.2 Flat 1
850 62 F ASY 160 164 0 LVH 145 N 6.2 Down 1
900 58 M ASY 114 318 0 ST 140 N 4.4 Down 1 
df2 = df1[df1.Oldpeak<=(df1.Oldpeak.mean()+3*df1.Oldpeak.std())]
df2.shape
(909, 12)
df[df.RestingBP>(df.RestingBP.mean()+3*df.RestingBP.std())]
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
109 39 M ATA 190 241 0 Normal 106 N 0.0 Up 0
241 54 M ASY 200 198 0 Normal 142 Y 2.0 Flat 1
365 64 F ASY 200 0 0 Normal 140 Y 1.0 Flat 1
399 61 M NAP 200 0 1 ST 70 N 0.0 Flat 1
592 61 M ASY 190 287 1 LVH 150 Y 2.0 Down 1
732 56 F ASY 200 288 1 LVH 133 Y 4.0 Down 1
759 54 M ATA 192 283 0 LVH 195 N 0.0 Up 1 
df3 = df2[df2.RestingBP<=(df2.RestingBP.mean()+3*df2.RestingBP.std())]
df3.shape
(902, 12)
df.ChestPainType.unique()
array(['ATA', 'NAP', 'ASY', 'TA'], dtype=object)
df.RestingECG.unique()
array(['Normal', 'ST', 'LVH'], dtype=object)
df.ExerciseAngina.unique()
array(['N', 'Y'], dtype=object)
df.ST_Slope.unique()
array(['Up', 'Flat', 'Down'], dtype=object)

Handle text columns using label encoding and one hot encoding 

df4 = df3.copy()
df4.ExerciseAngina.replace(
    {
        'N': 0,
        'Y': 1
    },
    inplace=True)

df4.ST_Slope.replace(
    {
        'Down': 1,
        'Flat': 2,
        'Up': 3
    },
    inplace=True
)

df4.RestingECG.replace(
    {
        'Normal': 1,
        'ST': 2,
        'LVH': 3
    },
    inplace=True)

df4.head()
Age Sex ChestPainType RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease
0 40 M ATA 140 289 0 1 172 0 0.0 3 0
1 49 F NAP 160 180 0 1 156 0 1.0 2 1
2 37 M ATA 130 283 0 2 98 0 0.0 3 0
3 48 F ASY 138 214 0 1 108 1 1.5 2 1
4 54 M NAP 150 195 0 1 122 0 0.0 3 0 
df5 = pd.get_dummies(df4, drop_first=True)
df5.head()
Age RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope HeartDisease Sex_M ChestPainType_ATA ChestPainType_NAP ChestPainType_TA
0 40 140 289 0 1 172 0 0.0 3 0 1 1 0 0
1 49 160 180 0 1 156 0 1.0 2 1 0 0 1 0
2 37 130 283 0 2 98 0 0.0 3 0 1 1 0 0
3 48 138 214 0 1 108 1 1.5 2 1 0 0 0 0
4 54 150 195 0 1 122 0 0.0 3 0 1 0 1 0 
X = df5.drop("HeartDisease",axis='columns')
y = df5.HeartDisease

X.head()
Age RestingBP Cholesterol FastingBS RestingECG MaxHR ExerciseAngina Oldpeak ST_Slope Sex_M ChestPainType_ATA ChestPainType_NAP ChestPainType_TA
0 40 140 289 0 1 172 0 0.0 3 1 1 0 0
1 49 160 180 0 1 156 0 1.0 2 0 0 1 0
2 37 130 283 0 2 98 0 0.0 3 1 1 0 0
3 48 138 214 0 1 108 1 1.5 2 0 0 0 0
4 54 150 195 0 1 122 0 0.0 3 1 0 1 0 
from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
X_scaled
array([[-1.42896269,  0.46089071,  0.85238015, ...,  2.06757196,
        -0.53547478, -0.22914788],
       [-0.47545956,  1.5925728 , -0.16132855, ..., -0.4836591 ,
         1.86750159, -0.22914788],
       [-1.74679706, -0.10495034,  0.79657967, ...,  2.06757196,
        -0.53547478, -0.22914788],
       ...,
       [ 0.37209878, -0.10495034, -0.61703246, ..., -0.4836591 ,
        -0.53547478, -0.22914788],
       [ 0.37209878, -0.10495034,  0.35947592, ...,  2.06757196,
        -0.53547478, -0.22914788],
       [-1.64085227,  0.3477225 , -0.20782894, ..., -0.4836591 ,
         1.86750159, -0.22914788]])
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=20)
X_train.shape
(721, 13)
X_test.shape
(181, 13)

Train a model using standalone support vector machine and then using bagging 

from sklearn.svm import SVC
from sklearn.model_selection import cross_val_score

scores = cross_val_score(SVC(), X, y, cv=5)
scores.mean()
0.6906445672191528

Use bagging now with svm

from sklearn.ensemble import BaggingClassifier

bag_model = BaggingClassifier(estimator=SVC(), n_estimators=100, max_samples=0.8, random_state=0)
scores = cross_val_score(bag_model, X, y, cv=5)
scores.mean()
0.6839656230816453

As you can see above, using bagging in case of SVM doesn’t make much difference in terms of model accuracy. Bagging is effective when we have high variance and instable model such as decision tree. Let’s explore how bagging changes the performance for a decision tree classifier.

Train a model using decision tree and then using bagging 

from sklearn.tree import DecisionTreeClassifier

scores = cross_val_score(DecisionTreeClassifier(random_state=0), X, y, cv=5)
scores.mean()
0.7193984039287907

Use bagging now with decision tree

bag_model = BaggingClassifier(
    estimator=DecisionTreeClassifier(random_state=0), 
    n_estimators=100, 
    max_samples=0.9, 
    oob_score=True,
    random_state=0
)

scores = cross_val_score(bag_model, X, y, cv=5)
scores.mean()
0.8037016574585636

You can see that with bagging the score improved from 71.93% to 80.37%

Train a model using Random Forest which itself uses bagging underneath 

from sklearn.ensemble import RandomForestClassifier

scores = cross_val_score(RandomForestClassifier(), X, y, cv=5)
scores.mean()
0.826998158379374

Boosting

from sklearn.ensemble import AdaBoostClassifier
clf = AdaBoostClassifier(
    n_estimators=100,
    random_state=0,
    algorithm='SAMME')
clf.fit(X_train, y_train)
AdaBoostClassifier(algorithm='SAMME', n_estimators=100, random_state=0)
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clf.score(X_test, y_test)
0.8397790055248618

Random forest gave even a better performance with 81.7% as score. Underneath it used bagging where it sampled not only data rows but also the columns (or features)

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