Scikit-Learn RandomizedSearchCV ElasticNet

Hyperparameter tuning is essential for optimizing machine learning models. In this example, we’ll demonstrate how to use scikit-learn’s RandomizedSearchCV for hyperparameter tuning of an ElasticNet model, suitable for regression tasks.

Random search is a method for evaluating different combinations of model hyperparameters. Unlike grid search, it samples a fixed number of hyperparameter combinations from a specified distribution, making it more efficient when searching over a large hyperparameter space.

ElasticNet combines the properties of Lasso and Ridge regression, allowing for both variable selection and regularization. It is useful when some features are correlated and when we want to control the complexity of the model.

Key hyperparameters for ElasticNet include alpha, which controls the overall strength of the regularization, and l1_ratio, which balances between Lasso (L1) and Ridge (L2) regularization. An l1_ratio of 0 corresponds to Ridge regression, and an l1_ratio of 1 corresponds to Lasso regression.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import ElasticNet
from scipy.stats import uniform

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=20, n_informative=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 hyperparameter distribution
param_dist = {
    'alpha': uniform(loc=0, scale=1),
    'l1_ratio': uniform(loc=0, scale=1)
}

# Perform random search
random_search = RandomizedSearchCV(estimator=ElasticNet(random_state=42),
                                   param_distributions=param_dist,
                                   n_iter=100,
                                   cv=5,
                                   scoring='neg_mean_squared_error',
                                   random_state=42)
random_search.fit(X_train, y_train)

# Report best score and parameters
print(f"Best score: {random_search.best_score_:.3f}")
print(f"Best parameters: {random_search.best_params_}")

# Evaluate on test set
best_model = random_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: -0.044
Best parameters: {'alpha': 0.020584494295802447, 'l1_ratio': 0.9699098521619943}
Test set R^2 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 a hyperparameter distribution with different values for alpha and l1_ratio hyperparameters.
4. Perform random search using RandomizedSearchCV, specifying the ElasticNet model, hyperparameter distribution, 100 iterations, 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 random search.
6. Evaluate the best model on the hold-out test set and report the R² score.

By using RandomizedSearchCV, we can efficiently 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.