Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 1, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
Embedded systems modelling project about directing a plane. Done in Erasmus at ETSISI, Universidad Politecnica de Madrid with 2 more contributors (from Spain and Romania).
Open and extensible benchmark for XAI methods
moDel Agnostic Language for Exploration and eXplanation
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
An interactive framework to visualize and analyze your AutoML process in real-time.
Effector - a Python package for global and regional effect methods
A Julia package for interpretable machine learning with stochastic Shapley values
Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
Model Agnostics breakDown plots
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"
💡 Adversarial attacks on explanations and how to defend them
SDK для работы с API IML delivery (api.iml.ru)
Interactive XAI dashboard
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