Release Version 0.6.0 · interpretml/interpret

v0.6.0 - 2024-03-16

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.

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.

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.

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