📍 Interactive Studio for Explanatory Model Analysis
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Updated
Aug 31, 2023 - R
📍 Interactive Studio for Explanatory Model Analysis
Explaining the output of machine learning models with more accurately estimated Shapley values
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Fast approximate Shapley values in R
Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020
Explainable Machine Learning in Survival Analysis
R package for SHAP plots
Efficient R implementation of SHAP
Machine Learning Finite State Machine Models from Data with Genetic Algorithms
Implementation of the Anchors algorithm: Explain black-box ML models
R implementation of Contextual Importance and Utility for Explainable AI
Repository for the familiar R-package. Familiar implements an end-to-end pipeline for interpretable machine learning of tabular data.
Variable importance via oscillations
Robust regression algorithm that can be used for explaining black box models (R implementation)
Network-guided greedy decision forest for feature subset selection
A method for conditional shapley value estimation, built off the shapr package: https://github.com/NorskRegnesentral/shapr/tree/master
Implementation of the mSHAP algorithm for explaining two-part models, as described by Matthews and Hartman (2021).
Examines fairness metrics for models including gender stereotyping versus group differences due to appropriate predictors. Also explores feature bias mitigation
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