bias variance tradeoff

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge
from sklearn.metrics import mean_squared_error

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Scale the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Train the Ridge regression model with different regularization strengths
alphas = [0.001, 0.01, 0.1, 1, 10]
for alpha in alphas:
    ridge = Ridge(alpha=alpha)
    ridge.fit(X_train_scaled, y_train)
    
    y_train_pred = ridge.predict(X_train_scaled)
    y_test_pred = ridge.predict(X_test_scaled)
    train_mse = mean_squared_error(y_train, y_train_pred)
    test_mse = mean_squared_error(y_test, y_test_pred)
    print(f"Alpha: {alpha}, Training MSE: {train_mse:.2f}, Testing MSE: {test_mse:.2f}")