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.
⬛ Python Individual Conditional Expectation Plot Toolbox
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Interpretability and explainability of data and machine learning models
Code for "High-Precision Model-Agnostic Explanations" paper
The main features of marcotcr/anchor are: Explainable AI Libraries, Model Interpretation.
Projects with overlapping indexed features include: ibm/aix360 — Interpretability and explainability of data and machine learning models. interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training… 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…
