Scikit-Learn RandomizedSearchCV MultiTaskElasticNet

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 a MultiTaskElasticNet model, commonly used for multi-output 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.

MultiTaskElasticNet is a linear model that extends ElasticNet for multi-task learning, enforcing both l1 and l2 regularization to enhance model robustness and feature selection.

Key hyperparameters for MultiTaskElasticNet include the regularization parameter alpha, the ElasticNet mixing parameter l1_ratio, and the max_iter parameter, which controls the maximum number of iterations for convergence.

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

# Generate synthetic multi-output regression dataset
X, y = make_regression(n_samples=1000, n_features=20, n_targets=3, 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 the model
model = MultiTaskElasticNet(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'alpha': uniform(loc=0, scale=1),
    'l1_ratio': uniform(loc=0, scale=1),
    'max_iter': [1000, 2000, 3000]
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=100,
                                   cv=5,
                                   scoring='r2',
                                   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_
r2_score = best_model.score(X_test, y_test)
print(f"Test set R^2 score: {r2_score:.3f}")

Running the example gives an output like:

Best score: 1.000
Best parameters: {'alpha': 0.04360377175443375, 'l1_ratio': 0.994550510797341, 'max_iter': 2000}
Test set R^2 score: 1.000

The steps are as follows:

1. Generate a synthetic multi-output 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 model then the hyperparameter distribution with different values for alpha, l1_ratio, and max_iter.
4. Perform random search using RandomizedSearchCV, specifying the MultiTaskElasticNet model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and an appropriate 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 performance.

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 MultiTaskElasticNet model.