[v0.4.0] - 2023-05-11
Added
- alternative objective functions: poisson_deviance, tweedie_deviance, gamma_deviance, pseudo_huber, rmse_log (log link)
- greediness __init__ parameter that allows selecting a behavior between cyclic boosting and greedy boosting
- smoothing_rounds __init__ parameter
- added type hints to the EBM __init__ parameters and class attributes
- init_score parameter to allow boosting and prediction on top of a previous model
- multiclass support in merge_ebms
- ability to monotonize features using post process model editing
Changed
- default BaseLinear regressor is changed from Lasso to LinearRegression class
- placed limits on the amount of memory used to find interactions with high cardinality categoricals
Fixed
- validation_size of 0 is now handled by disabling early_stopping and using the final model
Breaking Changes
- replaced the __init__ param "mains" with "exclude"
- removed the binning __init__ param as this functionality was already fully supported in feature_types
- removed the unused zero_val_count attribute and n_samples attribute
- renamed the noise_scale_ attribute to noise_scale_boosting_ and added noise_scale_binning_ to DPEBMs