Efficient R implementation of SHAP
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Updated
May 26, 2024 - R
Efficient R implementation of SHAP
Repository for the familiar R-package. Familiar implements an end-to-end pipeline for interpretable machine learning of tabular data.
An R package which provides a a neural network framework based on Generalized Additive Models
Explaining the output of machine learning models with more accurately estimated Shapley values
Explainable Machine Learning in Survival Analysis
Fast approximate Shapley values in R
R package for SHAP plots
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
A method for conditional shapley value estimation, built off the shapr package: https://github.com/NorskRegnesentral/shapr/tree/master
Examines fairness metrics for models including gender stereotyping versus group differences due to appropriate predictors. Also explores feature bias mitigation
📍 Interactive Studio for Explanatory Model Analysis
Robust regression algorithm that can be used for explaining black box models (R implementation)
Explaining black-box models through counterfactual paths and conditional permutations
R implementation of Contextual Importance and Utility for Explainable AI
Network-guided greedy decision forest for feature subset selection
Implementation of the mSHAP algorithm for explaining two-part models, as described by Matthews and Hartman (2021).
Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020
Machine Learning Finite State Machine Models from Data with Genetic Algorithms
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