Is your feature request related to a problem?

It's not possible to configure in the elastic net regression model whether it should apply more L1 or L2 regularization.

Desired solution

• Add a new parameter lasso_ratio: float to the initializer of safeds.ml.regression.ElasticNetRegression
• Raise a value error if lasso_ratio < 0 or lasso_ratio > 1
• Warn if lasso_ratio == 0 -> Users should use RidgeRegression instead
• Warn if lasso_ratio == 1 -> Users should use LassoRegression instead
• Pass it along to l1_ratio of the wrapped scikit-learn model in the fit method

Possible alternatives (optional)

No response

Screenshots (optional)

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Additional Context (optional)

• Use warnings.warn to warn.
• Use with pytest.warns to test that the warning is created.