Tuning parameters of the classifier used by BaggingClassifier

Question

Say that I want to train BaggingClassifier that uses DecisionTreeClassifier:

dt = DecisionTreeClassifier(max_depth = 1)
bc = BaggingClassifier(dt, n_estimators = 500, max_samples = 0.5, max_features = 0.5)
bc = bc.fit(X_train, y_train)

I would like to use GridSearchCV to find the best parameters for both BaggingClassifier and DecisionTreeClassifier (e.g. max_depth from DecisionTreeClassifier and max_samples from BaggingClassifier), what is the syntax for this?

Answer 1

I found the solution myself:

param_grid = {
    'base_estimator__max_depth' : [1, 2, 3, 4, 5],
    'max_samples' : [0.05, 0.1, 0.2, 0.5]
}

clf = GridSearchCV(BaggingClassifier(DecisionTreeClassifier(),
                                     n_estimators = 100, max_features = 0.5),
                   param_grid, scoring = choosen_scoring)
clf.fit(X_train, y_train)

i.e. saying that max_depth "belongs to" __ the base_estimator, i.e. my DecisionTreeClassifier in this case. This works and returns the correct results.