Scikit-Learn GridSearchCV AdaBoostRegressor

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 AdaBoostRegressor, a popular 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.

AdaBoostRegressor is an ensemble learning method that combines multiple weak learners to create a strong predictive model. It builds the model iteratively by fitting it to the residual errors of the previous model, reducing bias and variance.

The key hyperparameters for AdaBoostRegressor include n_estimators, which is the number of boosting stages; learning_rate, which shrinks the contribution of each regressor; and loss, which is the loss function to be optimized.

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

# 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 = {
    'n_estimators': [50, 100, 200],
    'learning_rate': [0.01, 0.1, 1],
    'loss': ['linear', 'square', 'exponential']
}

# Perform grid search
grid_search = GridSearchCV(estimator=AdaBoostRegressor(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: -3006.159
Best parameters: {'learning_rate': 1, 'loss': 'square', 'n_estimators': 200}
Test set R^2 score: 0.845

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 n_estimators, learning_rate, and loss hyperparameters.
4. Perform grid search using GridSearchCV, specifying the AdaBoostRegressor 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 efficiently explore different hyperparameter settings and find the combination that optimizes the model’s performance. This automated approach saves time and effort compared to manual tuning and helps ensure we select the best configuration for our AdaBoostRegressor model.