Releases
v0.4.0
[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
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