Python Libraries for Explainable AI
- 01. SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.
- 02. Local Interpretable Model-Agnostic Explanations (LIME), is an algorithm that can explain the predictions of any classifier or regressor in a faithful way, by approximating it locally with an interpretable model.
- 03. ELI5 is a Python library which allows to visualize and debug various Machine Learning models using unified API.
- 04. Shapash is a Python library dedicated to the interpretability of Data Science models.
- 05. Anchors is a method for generating human-interpretable rules that can be used to explain the predictions of a machine learning model.
- 06. XAI is a Machine Learning library that is designed with AI explainability in its core.
- 07. 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.
- 08. interpret-text - A library that incorporates state-of-the-art explainers for text-based machine learning models and visualizes the result with a built-in dashboard.
- 09. AI Explainability 360 - This extensible open source toolkit can help you comprehend how machine learning models predict labels by various means throughout the AI application lifecycle.
- 10. OmniXAI (short for Omni eXplainable AI), addresses several problems with interpreting judgments produced by machine learning models in practice.