v0.6.0 - 2024-03-16
Added
- Documentation on recommended hyperparameters to help users optimize their models.
- Support for monotone_constraints during model fitting, although post-processed monotonization is still suggested/preferred.
- The EBMModel class now includes _more_tags for better integration with the scikit-learn API, thanks to contributions from @DerWeh.
Changed
- Default max_rounds parameter increased from 5,000 to 25,000, for improved model accuracy.
- Numerous code simplifications, additional tests, and enhancements for scikit-learn compatibility, thanks to @DerWeh.
- The greedy boosting algorithm has been updated to support variable-length greedy sections, offering more flexibility during model training.
- Full compatibility with Python 3.12.
- Removal of the DecisionListClassifier from our documentation, as the skope-rules package seems to no longer be actively maintained.
Fixed
- The sweep function now properly returns self, correcting an oversight identified by @alvanli.
- Default exclude parameter set to None, aligning with scikit-learn's expected defaults, fixed by @DerWeh.
- A potential bug when converting features from categorical to continuous values has been addressed.
- Updated to handle the new return format for TreeShap in the SHAP 0.45.0 release.
Breaking Changes
- replaced the greediness __init__ parameter with greedy_ratio and cyclic_progress parameters for better control of the boosting process
(see documentation for notes on greedy_ratio and cyclic_progress) - replaced breakpoint_iteration_ with best_iteration_, which now contains the number of boosting steps rather than the number of boosting rounds