An interactive framework to visualize and analyze your AutoML process in real-time.
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Updated
May 15, 2024 - Python
An interactive framework to visualize and analyze your AutoML process in real-time.
Effector - a Python package for global and regional effect methods
Fit interpretable models. Explain blackbox machine learning.
moDel Agnostic Language for Exploration and eXplanation
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)
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Interactive XAI dashboard
Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)
A Python package with explanation methods for extraction of feature interactions from predictive models
Application of predictive models on a real data set of the obstetric medicine field and methods of interpretability on the previously fitted XGBoost model.
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