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.
A toolbox to iNNvestigate neural networks' predictions!
⬛ Python Individual Conditional Expectation Plot Toolbox
(a.k.a. Path-Integrated Gradients, a.k.a. Axiomatic Attribution for Deep Networks)
The main features of ankurtaly/integrated-gradients are: Explainable AI Libraries, Model Interpretability.
Projects with overlapping indexed features include: csinva/imodels — Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). aerdem4/lofo-importance — Leave One Feature Out Importance. albermax/innvestigate — A toolbox to iNNvestigate neural networks' predictions! andosa/treeinterpreter — TreeInterpreter. austinrochford/pycebox — ⬛ Python Individual Conditional Expectation Plot Toolbox. ethicalml/xai — XAI - An eXplainability toolbox for machine learning.
