Scikit-Learn GridSearchCV BaggingClassifier

Hyperparameter tuning is a crucial step in optimizing machine learning models for best performance. In this example, we’ll demonstrate how to use scikit-learn’s GridSearchCV to perform hyperparameter tuning for BaggingClassifier, an ensemble method that improves model stability and accuracy by combining the predictions of multiple base estimators.

Grid search is a method for evaluating different combinations of model hyperparameters to find the best performing configuration. It exhaustively searches through a specified parameter grid, trains and evaluates the model for each combination using cross-validation, and selects the hyperparameters that yield the best performance metric.

BaggingClassifier is an ensemble algorithm that builds multiple base estimators (often decision trees) on various subsets of the dataset and aggregates their predictions to form a final output. This approach reduces variance and helps prevent overfitting.

The key hyperparameters for BaggingClassifier include the number of base estimators (n_estimators), the fraction of samples to draw from the dataset to train each base estimator (max_samples), and the fraction of features to draw for each base estimator (max_features).

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, random_state=42)

# Split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Define parameter grid
param_grid = {
    'n_estimators': [10, 50, 100],
    'max_samples': [0.5, 1.0],
    'max_features': [0.5, 1.0]
}

# Perform grid search
grid_search = GridSearchCV(estimator=BaggingClassifier(estimator=DecisionTreeClassifier()),
                           param_grid=param_grid,
                           cv=5,
                           scoring='accuracy')
grid_search.fit(X_train, y_train)

# Report best score and parameters
print(f"Best score: {grid_search.best_score_:.3f}")
print(f"Best parameters: {grid_search.best_params_}")

# Evaluate on test set
best_model = grid_search.best_estimator_
accuracy = best_model.score(X_test, y_test)
print(f"Test set accuracy: {accuracy:.3f}")

Running the example gives an output like:

Best score: 0.899
Best parameters: {'max_features': 0.5, 'max_samples': 1.0, 'n_estimators': 100}
Test set accuracy: 0.905

The steps are as follows:

1. Generate a synthetic binary classification dataset using scikit-learn’s make_classification function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with different values for n_estimators, max_samples, and max_features hyperparameters.
4. Perform grid search using GridSearchCV, specifying the BaggingClassifier model, parameter grid, 5-fold cross-validation, and accuracy scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by grid search.
6. Evaluate the best model on the hold-out test set and report the accuracy.

By using GridSearchCV, we can easily explore different hyperparameter settings and find the combination that maximizes the model’s performance. This automated approach saves time and effort compared to manual hyperparameter tuning and helps ensure we select the best configuration for our BaggingClassifier model.