Scikit-Learn RandomizedSearchCV Lars

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 Lars (Least Angle Regression) model, suitable 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.

Lars is a regression algorithm particularly useful for high-dimensional data and situations where the number of features is much greater than the number of samples. It is a forward stagewise model selection technique.

Key hyperparameters for Lars include n_nonzero_coefs, which is the target number of non-zero coefficients; eps, which determines the precision of the solution; and fit_intercept, which specifies whether to calculate the intercept for this model.

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

# Generate synthetic regression dataset
X, y = make_regression(n_samples=1000, n_features=20, 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 = Lars()

# Define hyperparameter distribution
param_dist = {
    'n_nonzero_coefs': randint(1, 20),
    'eps': uniform(1e-4, 1e-1),
    '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_
score = best_model.score(X_test, y_test)
print(f"Test set score: {score:.3f}")

Running the example gives an output like:

Best score: -0.010
Best parameters: {'eps': 0.04048361710580409, 'fit_intercept': False, 'n_nonzero_coefs': 17}
Test set score: 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 Lars model.
4. Set up the hyperparameter distributions for n_nonzero_coefs, eps, and fit_intercept.
5. Perform random search using RandomizedSearchCV, specifying the Lars model, hyperparameter distribution, 100 iterations, 5-fold cross-validation, and the negative mean squared error scoring metric.
6. Report the best cross-validation score and best set of hyperparameters found by random search.
7. 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 Lars model.