Releases: SelfExplainML/PiML-Toolbox
Releases · SelfExplainML/PiML-Toolbox
V0.6.0
- Support data loading by Spark
- Support H2O model registration
- Support data-dependent model interpretation and explanation
- Add Friedman’s H-statistic to post hoc explanation
- Add data integrity check and more outlier detection methods
- Add log loss and Brier score to binary classification metrics
- Add segmented performance diagnostics
- Add hyperparameter tuning by grid and randomized search
- Pipeline optimization, code cleanup, and bug fixes
V0.5.1
- Add model-free diagnostics test APIs.
- Add support for a wider range of computing environments, including Mac ARM.
- Enhance overfit diagnostic module to support AUC metric.
- Add sliced 1D plot as alternatives of 2D heatmap.
- Code refactorization and bug fixes.