How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
Interpretability and explainability of data and machine learning models
The main features of ibm/aix360 are: Explainable AI Libraries, Model Interpretation.
Projects with overlapping indexed features include: interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training… marcotcr/anchor — Code for "High-Precision Model-Agnostic Explanations" paper. austinrochford/pycebox — ⬛ Python Individual Conditional Expectation Plot Toolbox. benedekrozemberczki/shapley — The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021). andosa/treeinterpreter — TreeInterpreter. marcotcr/lime — This project is an agnostic model interpretability framework and explainability tool designed to provide local…
⬛ Python Individual Conditional Expectation Plot Toolbox
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training inherently transparent models and applying post-hoc explanation techniques to make machine learning predictions human-understandable. The framework distinguishes itself by integrating differential privacy into the training of interpretable models to prevent sensitive data from leaking through explanations. It also features a visualization tool for rendering interactive decision paths and model behavior. The library covers model explainability through feature importance calcu
