Scikit-Learn RandomizedSearchCV LassoLars

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 LassoLars 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.

LassoLars is a linear regression model that applies Lasso (L1) regularization using the Least Angle Regression (LARS) algorithm. This method is particularly useful for high-dimensional datasets as it enforces sparsity in the coefficients.

Key hyperparameters for LassoLars include:

• alpha: The regularization strength, which controls the amount of shrinkage applied to the coefficients.
• max_iter: The maximum number of iterations for the LARS algorithm.
• eps: The machine-precision regularization in the computation.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import LassoLars
from scipy.stats import uniform, 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 = LassoLars(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'alpha': uniform(loc=0.01, scale=1.0),
    'max_iter': randint(100, 1000),
    'eps': uniform(loc=0.0001, scale=0.001)
}

# 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 mean squared error: {mse:.3f}")

Running the example gives an output like:

Best score: -0.018
Best parameters: {'alpha': 0.02807536361552087, 'eps': 0.0005938937151834347, 'max_iter': 915}
Test set mean squared error: -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 LassoLars model.
4. Define the hyperparameter distribution with different values for alpha, max_iter, and eps.
5. Perform random search using RandomizedSearchCV, specifying the LassoLars model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and negative mean squared error as the scoring metric.
6. Report the best cross-validation score and the best set of hyperparameters found by random search.
7. 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 LassoLars model.