Scikit-Learn GridSearchCV GradientBoostingRegressor

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 GradientBoostingRegressor, a powerful algorithm for regression tasks.

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.

Gradient Boosting Regressor builds an ensemble of trees sequentially, where each tree corrects the errors of the previous ones. This results in a strong predictive model.

The key hyperparameters for GradientBoostingRegressor include the learning rate (learning_rate), which controls the contribution of each tree to the final model; the number of boosting stages (n_estimators), which determines the number of trees in the ensemble; and the maximum depth of individual trees (max_depth), which affects the complexity of the model.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import GradientBoostingRegressor

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=10, 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 parameter grid
param_grid = {
    'learning_rate': [0.01, 0.1, 0.2],
    'n_estimators': [100, 200, 300],
    'max_depth': [3, 5, 7]
}

# Perform grid search
grid_search = GridSearchCV(estimator=GradientBoostingRegressor(random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='neg_mean_squared_error')
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_
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: 920.482
Best parameters: {'learning_rate': 0.2, 'max_depth': 3, 'n_estimators': 300}
Test set R^2 score: 0.947

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 parameter grid with different values for learning_rate, n_estimators, and max_depth hyperparameters.
4. Perform grid search using GridSearchCV, specifying the GradientBoostingRegressor model, parameter grid, 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 grid search.
6. Evaluate the best model on the hold-out test set and report the R^2 score.

By using GridSearchCV, we can easily explore different hyperparameter settings and find the combination that minimizes prediction errors. This automated approach saves time and effort compared to manual hyperparameter tuning and helps ensure we select the best configuration for our GradientBoostingRegressor model.