glex - Global Explanations for Tree-Based Models
Global explanations for tree-based models by decomposing regression or classification functions into the sum of main components and interaction components of arbitrary order. Calculates SHAP values and q-interaction SHAP for all values of q for tree-based models such as xgboost.
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cpp
5.82 score 8 stars 39 scriptsrandomPlantedForest - Random Planted Forest: A Directly Interpretable Tree Ensemble
Implements Random Planted Forest, a directly interpretable tree ensemble for regression and classification introduced by Hiabu, Mammen and Meyer (2020) <doi:10.48550/arXiv.2012.14563>. Trees are restricted to a maximum number of interacting predictors, so the fitted model decomposes exactly into main effects and low-order interactions, a functional ANOVA decomposition that can be extracted for interpretation. Fitting and prediction can use several threads, and fitted models can be serialized.
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intelligibilityinterpretable-machine-learninginterpretable-mlmachine-learningmlrandom-forestcpp
5.67 score 5 stars 74 scripts