regularization ridge regression

from sklearn.linear_model import Ridge
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split

# Load the Digits dataset
digits = load_digits()
X = digits.data
y = digits.target

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create and fit the Ridge model
ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)

# Evaluate the model
train_score = ridge.score(X_train, y_train)
test_score = ridge.score(X_test, y_test)

print(f'Train score: {train_score}')
print(f'Test score: {test_score}')