BaggingClassifier: nuts and bolts

Ensemble Methods in Python

Román de las Heras

Data Scientist, Appodeal

Heterogeneous vs Homogeneous Functions

Heterogeneous Ensemble Function

het_est = HeterogeneousEnsemble(
    estimators=[('est1', est1), ('est2', est2), ...],
    # additional parameters
)

Homogeneous Ensemble Function

hom_est = HomogeneousEnsemble(
    est_base,
    n_estimators=chosen_number,
    # additional parameters
)

Bagging Classifier example:

# Instantiate the base estimator ("weak" model)
clf_dt = DecisionTreeClassifier(max_depth=3)

# Build the Bagging classifier with 5 estimators
clf_bag = BaggingClassifier(
    clf_dt,
    n_estimators=5
)

# Fit the Bagging model to the training set
clf_bag.fit(X_train, y_train)

# Make predictions on the test set
y_pred = clf_bag.predict(X_test)

Bagging Regressor example:

# Instantiate the base estimator ("weak" model)
reg_lr = LinearRegression()

# Build the Bagging regressor with 10 estimators
reg_bag = BaggingRegressor(
    reg_lr
)

# Fit the Bagging model to the training set
reg_bag.fit(X_train, y_train)

# Make predictions on the test set
y_pred = reg_bag.predict(X_test)

Calculate the individual predictions using all estimators for which an instance was out of the sample
Combine the individual predictions
Evaluate the metric on those predictions:
- Classification: accuracy
- Regression: R^2

clf_bag = BaggingClassifier(
    clf_dt,
    oob_score=True
)
clf_bag.fit(X_train, y_train)

print(clf_bag.oob_score_)

0.9328125

pred = clf_bag.predict(X_test)
print(accuracy_score(y_test, pred))

0.9625

Ensemble Methods in Python