📍 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
Fast approximate Shapley values in R
Explainable Machine Learning in Survival Analysis
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
SHAP Plots in R
Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020
Different SHAP algorithms
Repository for the familiar R-package. Familiar implements an end-to-end pipeline for interpretable machine learning of tabular data.
Implementation of the Anchors algorithm: Explain black-box ML models
Machine Learning Finite State Machine Models from Data with Genetic Algorithms
Variable importance via oscillations
R implementation of Contextual Importance and Utility for Explainable AI
Local Individual Conditional Expectation (localICE) is a local explanation approach from the field of eXplainable Artificial Intelligence (XAI)
Network-guided greedy decision forest for feature subset selection
Robust regression algorithm that can be used for explaining black box models (R implementation)
Implementation of the mSHAP algorithm for explaining two-part models, as described by Matthews and Hartman (2021).
Sensitivity Analysis for Understanding Complex Computational Models
Explaining black-box models through counterfactual paths and conditional permutations
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