ridge with cross validation in python

Ridge regression is a type of linear regression that adds a penalty term to the coefficients, restricting the model from overfitting the data. In scikit-learn, we can use the RidgeCV class to fit a Ridge regression model with cross-validated alpha selection.

Here's an example of how to use RidgeCV with cross-validation:

main.py
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import cross_val_score
from sklearn.datasets import load_boston

# Load the data
boston = load_boston()

# Initialize the model
ridge = RidgeCV(alphas=[0.1, 1.0, 10.0], cv=5)

# Fit the model with cross-validation
scores = cross_val_score(ridge, boston.data, boston.target, cv=5)

# Print the mean cross-validation score
print("Cross-validation score: {:.2f}".format(scores.mean()))
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In this example, we loaded the Boston Housing dataset, initialized a RidgeCV model with a few different values of alpha, and fit the model with 5-fold cross-validation. The cross_val_score function returns an array of scores for each fold, so we printed the mean score across all folds.