How this analysis was created: This summary and feature list were written by an AI model that read the project's README and public documentation pages. Each feature links to the documentation it came from; stars, license and language come straight from the GitHub API. The model does not read the source code, and the analysis is refreshed when the project is re-analysed. Learn more on our About page.
(a.k.a. Path-Integrated Gradients, a.k.a. Axiomatic Attribution for Deep Networks)
⬛ Python Individual Conditional Expectation Plot Toolbox
A toolbox to iNNvestigate neural networks' predictions!
The main features of albermax/innvestigate are: Explainable AI Libraries, Model Interpretability.
Open-source alternatives to albermax/innvestigate include: aerdem4/lofo-importance — Leave One Feature Out Importance. csinva/imodels — Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). andosa/treeinterpreter — TreeInterpreter. ankurtaly/integrated-gradients — (a.k.a. Path-Integrated Gradients, a.k.a. Axiomatic Attribution for Deep Networks). austinrochford/pycebox — ⬛ Python Individual Conditional Expectation Plot Toolbox. ethicalml/xai — XAI - An eXplainability toolbox for machine learning.
