Version 0.6.12

v0.6.12 - 2025-06-17

Changed

• support for numpy 2.3.x
• increased default number of interaction terms

v0.6.11

v0.6.11 - 2025-06-05

Changed

• increased max_rounds to 50,000
• possibly faster prediction in some scenarios (unverified)
• remove obsolete dash components from requirements

Version 0.6.10

v0.6.10 - 2025-03-26

Added

• reorder_classes function which allows reordering of the classes after fitting
• support for ARM based Linux

Changed

• changed default to max_leaves=2 for classification
• changed default to n_jobs=2
• changed default to outer_bags=14

Fixed

• restrict to dash 2.x since visualizations are not working on dash 3.x

Version 0.6.9

v0.6.9 - 2025-01-06

Added

• refitting of the intercept term after fitting the rest of the model to improve the intercept value
• new options for handling missing values: "low", "high", "separate", and "gain"
• use Fischer (1958) for handling categorical values. This is the same method employed by LightGBM.
• added new parameters to control overfitting of nominal categoricals: gain_scale, min_cat_samples, cat_smooth

Changed

• enable AVX-512 by default
• modified default EBM parameters: outer_bags=16, n_jobs=-1

Fixed

• fixed memory leak in the purification function

Version 0.6.7

v0.6.7 - 2024-11-27

Changed

• minimum python version increased to 3.9
• minimum numpy version increased to 1.25

Fixed

• removed scipy dependency to resolve Issue #588

Version 0.6.6

v0.6.6 - 2024-11-20

Changed

• added predict_with_uncertainty function by @degenfabian in PR #584
• handle mono-classification in SHAP by @degenfabian in PR #582
• improvements to tree building in C++

Fixed

• issue that develop/debug options were not being honored in Windows when 1<n_jobs in joblib
• fix several bugs in C++ from negative hessians or negative gain values caused by floating point noise

Version 0.6.5

v0.6.5 - 2024-10-23

Changed

• default EBM parameters changed to improve model performance
• switch to using exact versions of exp/log instead of the previously used approximate versions

Fixed

• fix issue where very large feature values fail in the UI PR #581 by @degenfabian

Version 0.6.4

v0.6.4 - 2024-09-28

Added

• support for regularization parameters reg_alpha, and reg_lambda in EBMs
• support for the parameter max_delta_step in EBMs
• improved fitting speed for most of the alternative objectives

Version 0.6.3

v0.6.3 - 2024-08-07

Added

• visualizations for the APRL (Automatic Piecewise Linear Regression) package by @mathias-von-ottenbreit

Changed

• early_stopping_tolerance default changed to 1e-5 to reduce EBMs fitting time slightly
• shuffle initial feature order within each bag and during greedy boosting

Fixed

• fixed numpy 2.0 issue in the Marginal class

Version 0.6.2

v0.6.2 - 2024-06-22

Added

• pass optional kwargs to DecisionTreeClassifier in PR #537 by @busFred
• support for multiclass purification
• support for higher dimensional purification
• allow higher levels of purification than would be supported via the tolerance parameter

Changed

• numpy 2.0 support for EBMs
• update documentation regarding monotonicity in PR #531 by @Krzys25
• moved purification utility from "interpret/glassbox/_ebm/_research" to "interpret.utils"

Fixed

• possible fix for issue #543 where merge_ebms was creating unexpected NaN values