Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 1, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
moDel Agnostic Language for Exploration and eXplanation
H2O.ai Machine Learning Interpretability Resources
💡 Adversarial attacks on explanations and how to defend them
📍 Interactive Studio for Explanatory Model Analysis
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Model Agnostics breakDown plots
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model
Unofficial implementation of MVSS-Net (ICCV 2021) with Pytorch including training code.
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.
Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN
An interactive framework to visualize and analyze your AutoML process in real-time.
A Julia package for interpretable machine learning with stochastic Shapley values
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