How do I tune hyperparameters for Lasso and Ridge regression?

How do I tune hyperparameters for Lasso and Ridge regression?

To tune hyperparameters for Lasso and Ridge regression, you can use techniques like Grid Search or Random Search.

What specific hyperparameters should I focus on?

For Lasso, focus on the alpha parameter, and for Ridge, also focus on the alpha parameter, which controls regularization strength.

How do I implement Grid Search for these models?

You can use the GridSearchCV function from scikit-learn, specifying the model, parameter grid, and cross-validation strategy.

Can you provide a code example for that?

Sure! Here's a simple example:

from sklearn.linear_model import Lasso, Ridge
from sklearn.model_selection import GridSearchCV

# Define the model
model = Lasso()

# Define the parameter grid
param_grid = {'alpha': [0.1, 1.0, 10.0]}

# Set up Grid Search
grid_search = GridSearchCV(model, param_grid, cv=5)
grid_search.fit(X, y)

# Best parameters
print(grid_search.best_params_)