Scikit-Learn GridSearchCV LassoLars

Hyperparameter tuning is a crucial step in optimizing machine learning models for best performance. In this example, we’ll demonstrate how to use scikit-learn’s GridSearchCV to perform hyperparameter tuning for LassoLars, a regression model that combines Lasso and Least Angle Regression (LARS) for high-dimensional data.

Grid search is a method for evaluating different combinations of model hyperparameters to find the best performing configuration. It exhaustively searches through a specified parameter grid, trains and evaluates the model for each combination using cross-validation, and selects the hyperparameters that yield the best performance metric.

LassoLars is a regression algorithm that performs L1 regularization, which can zero out coefficients to create a sparse model. This is particularly useful for feature selection in high-dimensional data.

The key hyperparameters for LassoLars include the regularization strength (alpha), which controls the model complexity. A higher alpha value implies stronger regularization and a sparser model.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.linear_model import LassoLars

# 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 parameter grid
param_grid = {
    'alpha': [0.01, 0.1, 1, 10]
}

# Perform grid search
grid_search = GridSearchCV(estimator=LassoLars(),
                           param_grid=param_grid,
                           cv=5,
                           scoring='neg_mean_squared_error')
grid_search.fit(X_train, y_train)

# Report best score and parameters
print(f"Best score: {grid_search.best_score_:.3f}")
print(f"Best parameters: {grid_search.best_params_}")

# Evaluate on test set
best_model = grid_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: -0.011
Best parameters: {'alpha': 0.01}
Test set score: 1.000

The steps are as follows:

1. Generate a synthetic regression dataset using make_regression.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with different values for the alpha hyperparameter.
4. Perform grid search using GridSearchCV, specifying the LassoLars model, parameter grid, 5-fold cross-validation, and negative mean squared error as the scoring metric.
5. Report the best cross-validation score and best set of hyperparameters found by grid search.
6. Evaluate the best model on the hold-out test set and report the score.

Using GridSearchCV for LassoLars helps identify the optimal regularization strength, balancing model complexity and performance on unseen data.