Scikit-Learn GridSearchCV ElasticNet

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 ElasticNet, 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.

ElasticNet is a linear regression model that combines L1 and L2 regularization to improve performance on datasets with multiple features. It aims to overcome the limitations of both Ridge and Lasso regression by balancing the benefits of both types of regularization.

The key hyperparameters for ElasticNet include the regularization strength (alpha), which controls the overall strength of regularization; the L1 ratio (l1_ratio), which determines the balance between L1 and L2 regularization; and the maximum number of iterations (max_iter), which sets the maximum number of iterations for convergence.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import ElasticNet

# 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 = {
    'alpha': [0.1, 1.0, 10.0],
    'l1_ratio': [0.1, 0.5, 0.9],
    'max_iter': [1000, 2000]
}

# Perform grid search
grid_search = GridSearchCV(estimator=ElasticNet(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 score: {test_score:.3f}")

Running the example gives an output like:

Best score: -2.704
Best parameters: {'alpha': 0.1, 'l1_ratio': 0.9, 'max_iter': 1000}
Test set score: 1.000

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 alpha, l1_ratio, and max_iter hyperparameters.
4. Perform grid search using GridSearchCV, specifying the ElasticNet model, parameter grid, 5-fold cross-validation, and negative mean squared error 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 score.

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 ElasticNet model.