Interesting resources related to XAI (Explainable Artificial Intelligence)
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Updated
May 31, 2022 - R
Interesting resources related to XAI (Explainable Artificial Intelligence)
📍 Interactive Studio for Explanatory Model Analysis
Fast approximate Shapley values in R
Explainable Machine Learning in Survival Analysis
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020
Efficient R implementation of SHAP
Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning
Machine learning explanations
Variable importance via oscillations
Package for heterogeneous causal effects in the presence of imperfect compliance (e.g., instrumental variables, fuzzy regression discontinuity designs)
Workshop (2-6 hours): cleaning, missing value imputation, EDA, ensemble learning, calibration, variable importance ranking, accumulated local effect plots. WIP.
Package for heterogeneous treatment and spillover effects under network interference
Package with data, scripts and plots for manuscript "A comparison of machine learning and statistical species distribution models: when overfitting hurts interpretation" (submitted to Ecological Modelling, Dec 2022)
An R package providing functions for interpreting and distilling machine learning models
Robust regression algorithm that can be used for explaining black box models (R implementation)
Interpretable machine learning algorithm
Interpretable Configurational Regression: An R Package
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