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A toolbox to iNNvestigate neural networks' predictions!
(a.k.a. Path-Integrated Gradients, a.k.a. Axiomatic Attribution for Deep Networks)
The main features of google/explaining-in-style are: Model Interpretability.
Projects with overlapping indexed features include: albermax/innvestigate — A toolbox to iNNvestigate neural networks' predictions! 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. csinva/imodels — Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible). aerdem4/lofo-importance — Leave One Feature Out Importance.
