Scikit-Learn RandomizedSearchCV BaggingClassifier

Hyperparameter tuning is essential for optimizing machine learning models. In this example, we’ll demonstrate how to use scikit-learn’s RandomizedSearchCV for hyperparameter tuning of a BaggingClassifier, a popular ensemble method.

Random search is a method for evaluating different combinations of model hyperparameters. Unlike grid search, it samples a fixed number of hyperparameter combinations from a specified distribution, making it more efficient when searching over a large hyperparameter space.

BaggingClassifier is an ensemble meta-estimator that fits base classifiers on random subsets of the original dataset and then aggregates their predictions. This approach helps reduce variance and improve the robustness of the model.

Key hyperparameters for BaggingClassifier include the number of base classifiers (n_estimators), which determines how many models are aggregated; max_samples, which is the maximum number of samples drawn from the training set for each base classifier; and max_features, which is the maximum number of features drawn from the training set for each base classifier.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.ensemble import BaggingClassifier
from scipy.stats import randint, uniform

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=100, n_features=20, n_informative=10, n_redundant=10, 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 the model
model = BaggingClassifier(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'n_estimators': randint(10, 50),
    'max_samples': uniform(0.1, 0.9),
    'max_features': uniform(0.1, 0.9)
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=50,
                                   cv=5,
                                   scoring='accuracy',
                                   random_state=42)
random_search.fit(X_train, y_train)

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

# Evaluate on test set
best_model = random_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: 0.912
Best parameters: {'max_features': 0.4693446307320668, 'max_samples': 0.7799960246887438, 'n_estimators': 45}
Test set accuracy: 0.850

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 BaggingClassifier model and specify the hyperparameter distributions for n_estimators, max_samples, and max_features.
4. Perform random search using RandomizedSearchCV, specifying the BaggingClassifier model, hyperparameter distributions, 100 iterations, 5-fold cross-validation, and accuracy as the scoring metric.
5. Report the best cross-validation score and the best set of hyperparameters found by random search.
6. Evaluate the best model on the hold-out test set and report the accuracy.

By using RandomizedSearchCV, we can efficiently 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.