Scikit-Learn RandomizedSearchCV LassoLarsIC

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 LassoLarsIC model, commonly used 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.

LassoLarsIC is a regression model that uses the Least Angle Regression algorithm with L1 regularization. It automatically selects the best alpha parameter based on information criteria (AIC or BIC), providing a balance between model complexity and fit.

Key hyperparameters for LassoLarsIC include the information criterion (criterion) used to select the alpha parameter, which can be AIC or BIC; fit_intercept, which determines whether the intercept should be fitted or set to zero; and normalize, which decides whether to normalize the input features before fitting the model.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import LassoLarsIC
from scipy.stats import randint

# 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 the model
model = LassoLarsIC()

# Define hyperparameter distribution
param_dist = {
    'criterion': ['aic', 'bic'],
    'fit_intercept': [True, False],
}

# Perform random search
random_search = RandomizedSearchCV(estimator=model,
                                   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_
mse = best_model.score(X_test, y_test)
print(f"Test set MSE: {mse:.3f}")

Running the example gives an output like:

Best score: -0.010
Best parameters: {'fit_intercept': True, 'criterion': 'aic'}
Test set MSE: 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 model then the hyperparameter distribution with different values for criterion, fit_intercept, and normalize.
4. Perform random search using RandomizedSearchCV, specifying the LassoLarsIC model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and 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 mean squared error.

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 LassoLarsIC model.