Explaining the output of machine learning models with more accurately estimated Shapley values
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Updated
May 27, 2024 - R
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
A method for conditional shapley value estimation, built off the shapr package: https://github.com/NorskRegnesentral/shapr/tree/master
This is the code and data to replicate the analysis in Serebrennikov, Skougarevskiy (2023).
Robust regression algorithm that can be used for explaining black box models (R implementation)
An R package providing functions for interpreting and distilling machine learning models
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Flexible tool for bias detection, visualization, and mitigation
Variable importance via oscillations
Make any model from the caret package explainable 🥕-->🔍-->📈
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