Scikit-Learn RandomizedSearchCV MultiTaskLasso

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 MultiTaskLasso model, commonly used for multitask 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.

MultiTaskLasso is a linear model that performs L1 regularization, enforcing sparsity across multiple tasks. This model fits multiple linear regression models jointly, which can lead to improved performance when the tasks are related.

Key hyperparameters for MultiTaskLasso include the regularization parameter (alpha), which controls the sparsity of the solution and affects model complexity and performance. The max_iter parameter defines the maximum number of iterations for the optimization algorithm.

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

# Generate synthetic multitask 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 = MultiTaskLasso(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'alpha': uniform(loc=0.01, scale=1.0),
    'max_iter': [1000, 2000, 3000]
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   param_distributions=param_dist,
                                   n_iter=50,
                                   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 score: {test_score:.3f}")

Running the example gives an output like:

Best score: -0.013
Best parameters: {'alpha': 0.03058449429580245, 'max_iter': 2000}
Test set score: 1.000

The steps are as follows:

1. Generate a synthetic multitask 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 and then the hyperparameter distribution with different values for alpha and max_iter.
4. Perform random search using RandomizedSearchCV, specifying the MultiTaskLasso model, hyperparameter distribution, 50 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 test set 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 MultiTaskLasso model.