Scikit-Learn GridSearchCV AdaBoostClassifier

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 AdaBoostClassifier, an ensemble learning algorithm that combines multiple weak classifiers to create a strong classifier.

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.

AdaBoostClassifier is an ensemble learning algorithm that builds a strong classifier by combining multiple weak classifiers. It iteratively trains classifiers, each focusing on the errors of the previous ones.

The key hyperparameters for AdaBoostClassifier include the number of weak learners to train (n_estimators), which controls the number of boosting rounds; the learning rate (learning_rate), which determines the weight applied to each classifier at each boosting step; and the boosting algorithm (algorithm), which can be either ‘SAMME’ or ‘SAMME.R’.

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

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, n_features=20, n_informative=15, n_redundant=5, 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],
    'algorithm': ['SAMME', 'SAMME.R']
}

# Perform grid search
grid_search = GridSearchCV(estimator=AdaBoostClassifier(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: {'algorithm': 'SAMME', 'learning_rate': 1, 'n_estimators': 200}
Test set accuracy: 0.830

The steps are as follows:

1. Generate a synthetic binary classification dataset using scikit-learn’s make_classification 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 algorithm hyperparameters.
4. Perform grid search using GridSearchCV, specifying the AdaBoostClassifier model, parameter grid, 5-fold cross-validation, and accuracy 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 accuracy.

By using GridSearchCV, we can efficiently explore different hyperparameter settings and identify the combination that optimizes the performance of the AdaBoostClassifier. This approach automates hyperparameter tuning, saving time and ensuring optimal model configuration.