bias and variance in machine learning

from sklearn.linear_model import Ridge

# Train a Ridge regression model
ridge = Ridge(alpha=1.0)
ridge.fit(X_train, y_train)

# Evaluate the model using cross-validation
cv_scores_ridge = cross_val_score(ridge, X_train, y_train, cv=5, scoring='neg_mean_squared_error')
cv_scores_ridge = -cv_scores_ridge

# Calculate the average MSE and its standard deviation
avg_mse_ridge = cv_scores_ridge.mean()
std_mse_ridge = cv_scores_ridge.std()

print(f"Ridge Cross-Validation MSE: {avg_mse_ridge:.2f} +/- {std_mse_ridge:.2f}")