Breaking Changes
- RandomForestClassifierExplainer and RandomForestRegressionExplainer will be
deprecated: can now simply use ClassifierExplainer or RegressionExplainer and the
mixin class will automatically be loaded.
New Features
- Now also support for visualizing individual trees for XGBoost models!
(XGBClassifier and XGBRegressor). The XGBExplainer mixin class will be
automatically loaded and make decisiontree_df(), decision_path() and plot_trees()
methods available, the dashboard Decision Trees tab and components now also work for
XGBoost models.
- new parameter n_jobs for calculations that can be parallelized (e.g. permutation importances)
- contrib_df, plot_shap_contributions: can order by global shap feature
importance with sort='importance' (as well as 'abs', 'high-to-low'
'low-to-high')
- added actual outcome to plot_trees (for both RandomForest and XGB)
Improvements
- optimized code for calculating permutation importance, adding possibility to calculate in parallel
- shap dependence component: if no color col selected, output standard blue dots instead of ignoring update
Other Changes
- added selenium browser based integration tests for dashboards (also working with github actions)
- added tests for multiclass classsification, DecisionTree and ExtraTrees models
- added tests for XGBExplainers
- added proper docstrings to explainer_methods.py