Scikit-Learn GridSearchCV StackingClassifier

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 StackingClassifier, an ensemble learning method that combines multiple base classifiers.

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.

StackingClassifier is an ensemble method that combines multiple base estimators (classifiers) and uses a final estimator to make predictions. This approach leverages the strengths of different models to improve overall predictive performance.

Key hyperparameters for StackingClassifier include the list of base estimators (estimators), the final estimator (final_estimator), and hyperparameters for both base and final estimators.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import StackingClassifier

# Generate synthetic binary classification dataset
X, y = make_classification(n_samples=1000, 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 base estimators
estimators = [
    ('lr', LogisticRegression()),
    ('dt', DecisionTreeClassifier()),
    ('knn', KNeighborsClassifier())
]

# Define parameter grid
param_grid = {
    'final_estimator__C': [0.1, 1, 10],
}

# Perform grid search
stacking_clf = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression())
grid_search = GridSearchCV(estimator=stacking_clf,
                           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: 0.915
Best parameters: {'final_estimator__C': 10}
Test set accuracy: 0.950

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 base estimators including LogisticRegression, DecisionTreeClassifier, and KNeighborsClassifier.
4. Define the parameter grid with different values for the final estimator’s hyperparameters.
5. Perform grid search using GridSearchCV, specifying the StackingClassifier, parameter grid, 5-fold cross-validation, and accuracy scoring metric.
6. Report the best cross-validation score and best set of hyperparameters found by grid search.
7. Evaluate the best model on the hold-out test set and report the accuracy.

By using GridSearchCV, we can easily explore different hyperparameter settings for the StackingClassifier 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 ensemble model.