Interpretable Machine Learning Collection

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🔥Fire

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“Fit interpretable models. Explain blackbox machine learning. ”

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“A curated list of awesome responsible machine learning resources.”

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🔮Psychic

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“Generate Diverse Counterfactual Explanations for any machine learning model. ”

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🔮Psychic

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“moDel Agnostic Language for Exploration and eXplanation”

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“PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics”

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🔮Psychic

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“[CONTRIBUTORS WELCOME] Generalized Additive Models in Python”

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“OmniXAI: A Library for eXplainable AI”

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“Interesting resources related to XAI (Explainable Artificial Intelligence)”

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🔮Psychic

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“Shapley Interactions and Shapley Values for Machine Learning”

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“Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.”

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“H2O.ai Machine Learning Interpretability Resources”

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🔮Psychic

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“Explain & debug any blackbox machine learning model with a single line of code. ”

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🔮Psychic

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“A Python library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI :octocat:”

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“💡 Adversarial attacks on explanations and how to defend them”

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“📍 Interactive Studio for Explanatory Model Analysis”

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🔮Psychic

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“PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting”

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🔮Psychic

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“Concept Bottleneck Models, ICML 2020”