• Add support for rank-based feature selection in KernelExplainer.
• Depreciate l1_reg="auto" in KernelExplainer in favor of eventually defaulting to l1_reg="num_features(10)"
• New color scales based on the Lch color space.
• Better auto-color choices for multi-class summary plots.
• Better plotting of NaN values in dependence_plots
• Updates for Pytorch 1.0 courtesy of @gabrieltseng
• Fix the sklearn DecisionTreeClassifier handling to correctly normalize to a probability output
• Enable multi-output model support for TreeExplainer when feature_dependence="independent"
• Correctly load the objective of LightGBM models for use in explaining the model loss.
• Fix numerical precision mismatch with sklearn models.
• Fix numerical precision mismatch with XGBoost models by now directly loading from memory instead of JSON.