Scikit-Learn RandomizedSearchCV Lasso

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 Lasso regression model, commonly used for linear regression tasks with L1 regularization.

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.

Lasso regression is a linear model that uses L1 regularization to enforce sparsity in the model coefficients. This technique helps in feature selection by shrinking some coefficients to zero, effectively removing them from the model.

Key hyperparameters for Lasso regression include the regularization strength (alpha), which controls the degree of shrinkage applied to the coefficients. A higher alpha value leads to more regularization, while a lower alpha value results in less regularization.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, RandomizedSearchCV
from sklearn.linear_model import Lasso
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 the model
model = Lasso(random_state=42)

# Define hyperparameter distribution
param_dist = {
    'alpha': uniform(loc=0, scale=1)
}

# 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: -0.011
Best parameters: {'alpha': 0.005522117123602399}
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 model then the hyperparameter distribution with different values for alpha.
4. Perform random search using RandomizedSearchCV, specifying the Lasso 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 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 Lasso regression model.