Algorithms for explaining machine learning models
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
⬛ Python Individual Conditional Expectation Plot Toolbox
Bias Auditing & Fair ML Toolkit
Die Hauptfunktionen von dssg/aequitas sind: Explainability and Fairness, Model Interpretation.
Open-Source-Alternativen zu dssg/aequitas sind unter anderem: seldonio/alibi — Algorithms for explaining machine learning models. interpretml/interpret — Interpret is an interpretable machine learning library and glassbox model framework. It provides toolkits for training… andosa/treeinterpreter — TreeInterpreter. benedekrozemberczki/shapley — The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021). bourdakos1/capsnet-visualization — 🎆 A visualization of the CapsNet layers to better understand how it works. austinrochford/pycebox — ⬛ Python Individual Conditional Expectation Plot Toolbox.
