Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
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Updated
Apr 4, 2024 - Python
Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.
XAI - An eXplainability toolbox for machine learning
Neural network visualization toolkit for tf.keras
TrustyAI Explainability Toolkit
Principal Image Sections Mapping. Convolutional Neural Network Visualisation and Explanation Framework
XAI for yoloV8
SHAP Interaction Quantification (short SHAP-IQ) is an XAI framework extending on the well-known shap explanations by introducing interactions i.e. synergy scores.
Fast and incremental explanations for online machine learning models. Works best with the river framework.
a tool for comparing the predictions of any text classifiers
📺 A Python library for pruning and visualizing Keras Neural Networks' structure and weights
Model-agnostic Statistical/Machine Learning explainability (currently Python) for tabular data
Explain Neural Networks using Layer-Wise Relevance Propagation and evaluate the explanations using Pixel-Flipping and Area Under the Curve.
XAISuite: Train machine learning models, generate explanations, and compare different explanation systems with just a simple line of code.
A library that helps to explain AI models in a really quick & easy way
This repository contains the code for the XAIInferencerEngine PyPi library.
A scoring system for explainability
Xi method
Artificial Neural Networks for Java This package provides Object oriented Neural Networks for making Explainable Networks. Object Oriented Network structure is helpful for observing each and every element the model. This package is developed for XAI research and development.
Official implementation of GPX: Gaussian Process Regression with Interpretable Sample-wise Feature Weights (published on TNNLS)
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