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Regression workflows with linear, ridge, lasso, and elastic net

Dr. Elena Vasquez Apr 2026

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from sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet
from sklearn.metrics import mean_absolute_error, root_mean_squared_error

models = {
    'linear': LinearRegression(),
    'ridge': Ridge(alpha=1.0),
    'lasso': Lasso(alpha=0.01),
    'elastic_net': ElasticNet(alpha=0.01, l1_ratio=0.5),
}

for name, model in models.items():
    model.fit(X_train, y_train)
    predictions = model.predict(X_valid)
    mae = mean_absolute_error(y_valid, predictions)
    rmse = root_mean_squared_error(y_valid, predictions)
    print(name, round(mae, 3), round(rmse, 3))

1 file · python Explain with highlit

For numeric targets I usually start simple and make regularization earn its keep. Ridge is stable, Lasso helps with sparsity, and ElasticNet is a practical compromise when correlated features exist. The main goal is not just minimizing RMSE but understanding which variables carry usable signal.

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