BaggingClassifier: basis en praktijk

Ensemblemethoden in Python

Román de las Heras

Data Scientist, Appodeal

Heterogene vs. homogene functies

Heterogeen ensemble-functie

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

Homogeen ensemble-functie

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

Voorbeeld Bagging Classifier:

# 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)

Voorbeeld Bagging Regressor:

# 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)

Bereken de individuele voorspellingen van alle estimators waarvoor een instantie out-of-bag was
Combineer de individuele voorspellingen
Evalueer de metriek op die voorspellingen:
- Classificatie: accuracy
- Regressie: 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

Ensemblemethoden in Python