Scikit-Learn GridSearchCV GradientBoostingClassifier

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 GradientBoostingClassifier, a powerful ensemble learning algorithm for classification 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.

GradientBoostingClassifier is an ensemble learning method that builds multiple decision trees sequentially, where each tree corrects errors from the previous one. It is particularly effective for classification tasks.

The key hyperparameters for GradientBoostingClassifier include:

• n_estimators: The number of boosting stages to be run.
• learning_rate: Shrinks the contribution of each tree.
• max_depth: The maximum depth of the individual decision trees.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.ensemble import GradientBoostingClassifier

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=100, n_features=20, n_informative=10, n_redundant=10, 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],
    'learning_rate': [0.01, 0.1, 0.2],
    'max_depth': [3, 5, 7]
}

# Perform grid search
grid_search = GridSearchCV(estimator=GradientBoostingClassifier(random_state=42),
                           param_grid=param_grid,
                           cv=5,
                           scoring='accuracy')
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_
accuracy = best_model.score(X_test, y_test)
print(f"Test set accuracy: {accuracy:.3f}")

Running the example gives an output like:

Best score: 0.838
Best parameters: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 100}
Test set accuracy: 0.800

The steps are as follows:

1. Generate a synthetic binary classification dataset using make_classification.
2. Split the dataset into training and testing sets using train_test_split.
3. Define the parameter grid with values for n_estimators, learning_rate, and max_depth.
4. Perform grid search using GridSearchCV, specifying the GradientBoostingClassifier, parameter grid, 5-fold cross-validation, and accuracy as the scoring metric.
5. Report the best cross-validation score and the best set of hyperparameters found by grid search.
6. Evaluate the best model on the test set and report the accuracy.

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 GradientBoostingClassifier model.