Scikit-Learn RandomizedSearchCV BaggingRegressor

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 BaggingRegressor model, commonly used for reducing variance in regression tasks.

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.

BaggingRegressor is an ensemble method that reduces variance by averaging multiple base regressors, which are trained on different random subsets of the training data. This approach helps in improving the robustness and accuracy of the predictions.

Key hyperparameters for BaggingRegressor include the number of base regressors (n_estimators), which controls the ensemble size; the fraction of samples to draw (max_samples); and the fraction of features to draw (max_features). These hyperparameters help in fine-tuning the model for better performance.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.ensemble import BaggingRegressor
from scipy.stats import randint, uniform

# Generate synthetic regression dataset
X, y = make_regression(n_samples=100, n_features=20, noise=0.1, 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 = BaggingRegressor(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='neg_mean_squared_error',
                                   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_
test_score = best_model.score(X_test, y_test)
print(f"Test set R^2 score: {test_score:.3f}")

Running the example gives an output like:

Best score: -11457.526
Best parameters: {'max_features': 0.9745408858501934, 'max_samples': 0.8640224418394755, 'n_estimators': 14}
Test set R^2 score: 0.432

The steps are as follows:

1. Generate a synthetic regression dataset using scikit-learn’s make_regression function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the model then the hyperparameter distribution with different values for n_estimators, max_samples, and max_features.
4. Perform random search using RandomizedSearchCV, specifying the BaggingRegressor model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and negative mean squared error scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by random search.
6. Evaluate the best model on the hold-out test set and report the R^2 score.

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 BaggingRegressor model.