Scikit-Learn RandomizedSearchCV GradientBoostingRegressor

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 GradientBoostingRegressor model, commonly used for 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.

Gradient Boosting Regressor is an ensemble learning technique that builds multiple decision trees in a sequential manner, where each tree corrects the errors of its predecessor. It is highly effective for predictive modeling.

Key hyperparameters for Gradient Boosting Regressor include:

• n_estimators: The number of boosting stages to be run.
• learning_rate: Shrinks the contribution of each tree by the learning rate.
• max_depth: The maximum depth of the individual regression estimators.

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

# Generate synthetic regression dataset
X, y = make_regression(n_samples=100, 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 the model
model = GradientBoostingRegressor(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'n_estimators': randint(10, 50),
    'learning_rate': uniform(0.01, 0.2),
    'max_depth': randint(1, 10)
}

# 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_
mse = -best_model.score(X_test, y_test)
print(f"Test set Mean Squared Error: {mse:.3f}")

Running the example gives an output like:

Best score: -9327.056
Best parameters: {'learning_rate': 0.18744254851526532, 'max_depth': 1, 'n_estimators': 44}
Test set Mean Squared Error: -0.635

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, learning_rate, and max_depth.
4. Perform random search using RandomizedSearchCV, specifying the GradientBoostingRegressor model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and mean squared error as the 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 mean squared error.

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 Gradient Boosting Regressor model.