Scikit-Learn GridSearchCV HistGradientBoostingRegressor

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 HistGradientBoostingRegressor, 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.

HistGradientBoostingRegressor is a histogram-based gradient boosting algorithm that efficiently handles large datasets. It builds an ensemble of decision trees to predict continuous outcomes.

The key hyperparameters for HistGradientBoostingRegressor include learning_rate, which controls the contribution of each tree; max_iter, which defines the number of boosting iterations; and max_leaf_nodes, which specifies the maximum number of leaf nodes in each tree.

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

# 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 parameter grid
param_grid = {
    'learning_rate': [0.01, 0.1, 1],
    'max_iter': [50, 100],
    'max_leaf_nodes': [10, 20, 30]
}

# Perform grid search
grid_search = GridSearchCV(estimator=HistGradientBoostingRegressor(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: {test_score:.3f}")

Running the example gives an output like:

Best score: -11148.680
Best parameters: {'learning_rate': 0.1, 'max_iter': 100, 'max_leaf_nodes': 10}
Test set R^2: 0.612

The steps are as follows:

1. Generate a synthetic regression dataset using make_regression.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with different values for learning_rate, max_iter, and max_leaf_nodes.
4. Perform grid search using GridSearchCV, specifying the HistGradientBoostingRegressor model, parameter grid, 5-fold cross-validation, and negative mean squared error as the 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 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 HistGradientBoostingRegressor model.