New loss function and an optional mean bias correction

✨ New Features

• Huber Loss Function: A new huber loss function has been added to APLRRegressor. This provides a robust alternative to Mean Squared Error (MSE) that is less sensitive to outliers while remaining differentiable everywhere, improving model stability on noisy datasets. The delta parameter for this loss function is controlled via dispersion_parameter.

• Explicit Mean Bias Correction:

  • A new mean_bias_correction constructor parameter (default False) has been introduced to apply an explicit post-processing step to the model's intercept.
  • When enabled, this feature adjusts the intercept to make the model's predictions on the training data have the same (weighted) mean as the response variable. This can be particularly useful for loss functions like huber, which can otherwise produce biased predictions.
  • The correction is currently implemented for models using the identity and log link functions.

📚 Documentation

• The API reference for APLRRegressor has been updated to describe the above.