Smoothing and Mitigation of Boundary Problems by Regularization. Backwards Compatibility.

• Added ridge_penalty Hyperparameter: Introduced a new hyperparameter, ridge_penalty (default is 0.0001), which specifies the (weighted) ridge penalty applied to the model. Positive values can smooth model effects and help mitigate boundary problems, such as regression coefficients with excessively high magnitudes near the boundaries. To find the optimal value, consider using a grid search or similar tuning methods. Negative values are treated as zero. The default value of 0.0001 has been determined based on empirical tests on more than a hundred datasets.

• Changed Default Value for early_stopping_rounds: Updated the default value of the early_stopping_rounds hyperparameter from 500 to 200 to improve convergence speed.

• Introduced Backwards Compatibility: Enabled backwards compatibility for already trained models. It is now possible to load models (e.g., using pickle or joblib) as long as they were trained with APLR version 10.6.1 or newer.