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馃摎SHAP (SHapley Additive exPlanations)
SHAP is a model agnostic and works by breaking down the contribution of each feature and attributing a score to each feature.
馃摎 LIME (Local Interpretable Model-agnostic Explanations)
LIME is another model agnostic method that works by approximating the behavior of the model locally around a specific prediction.
馃摎 Eli5
Eli5 is a library for debugging and explaining classifiers. It provides feature importance scores, as well as "reason codes" for scikit-learn, Keras, xgboost, LightGBM, CatBoost.
馃摎 Shapash
Shapash is a Python library which aims to make machine learning interpretable and understandable to everyone. Shapash provides several types of visualization with explicit labels.
馃摎 Anchors
Anchors is a method for generating human-interpretable rules that can be used to explain the predictions of a machine learning model.
馃摎 XAI (eXplainable AI)
XAI is a library for explaining and visualizing the predictions of machine learning models including feature importance scores, decision trees, and rule-based explanations.
馃摎 BreakDown
BreakDown is a tool that can be used to explain the predictions of linear models. It works by decomposing the model's output into the contribution of each input feature.
馃摎 interpret-text
interpret-text is a library for explaining the predictions of natural language processing models.
馃摎 iml (Interpretable Machine Learning)
iml currently contains the interface and IO code from the Shap project, and it will potentially also do the same for the Lime project.
馃摎 aix360 (AI Explainability 360)
aix360 includes a comprehensive set of algorithms that cover different dimensions
馃摎 OmniXAI
OmniXAI (short for Omni eXplainable AI), addresses several problems with interpreting judgments produced by machine learning models in practice.