Fit interpretable models. Explain blackbox machine learning.
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Updated
Jun 26, 2024 - C++
Fit interpretable models. Explain blackbox machine learning.
An interactive framework to visualize and analyze your AutoML process in real-time.
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Application of predictive models on a real data set of the obstetric medicine field and methods of interpretability on the previously fitted XGBoost model.
Robustness of Global Feature Effect Explanations (ECML PKDD 2024)
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
moDel Agnostic Language for Exploration and eXplanation
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
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)
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