Scikit-Learn GridSearchCV LassoLarsIC

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 LassoLarsIC, a model designed for linear regression with built-in feature selection.

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.

LassoLarsIC is a linear regression model that performs automatic feature selection by minimizing the information criterion (AIC or BIC). It is particularly useful for high-dimensional data where feature selection is essential to improve model performance and interpretability.

The key hyperparameters for LassoLarsIC include the criterion, which specifies the information criterion to use (either ‘aic’ or ‘bic’), and fit_intercept, which determines whether to fit an intercept to the dataset.

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

# 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 parameter grid
param_grid = {
    'criterion': ['aic', 'bic'],
    'fit_intercept': [True, False]
}

# Perform grid search
grid_search = GridSearchCV(estimator=LassoLarsIC(),
                           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 R^2 score: {test_score:.3f}")

Running the example gives an output like:

Best score: -0.010
Best parameters: {'criterion': 'aic', 'fit_intercept': False}
Test set R^2 score: 1.000

The steps are as follows:

1. Generate a synthetic regression dataset using make_regression function.
2. Split the dataset into train and test sets using train_test_split.
3. Define the parameter grid with values for criterion and fit_intercept hyperparameters.
4. Perform grid search using GridSearchCV, specifying the LassoLarsIC model, parameter grid, 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 grid search.
6. Evaluate the best model on the hold-out test set and report the R^2 score.

By using GridSearchCV, we can efficiently explore different hyperparameter settings for LassoLarsIC and find the combination that optimizes model performance, ensuring a robust and effective linear regression model with built-in feature selection.