Open-source alternatives to Lofo Importance

A library for debugging/inspecting machine learning classifiers and explaining their predictions

A toolbox to iNNvestigate neural networks' predictions!

A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

XAI - An eXplainability toolbox for machine learning

SHAP is a machine learning explainer that uses a game-theoretic framework to estimate the contribution of each feature to a model prediction. It provides a set of tools for quantifying how individual input features push a specific output away from a baseline value. The project includes specialized explainers for different architectures, including high-speed implementations for decision trees and ensemble models, linearization algorithms for deep learning networks, and covariance integration for linear models. It also features a model-agnostic interpretability tool that uses a kernel method to

Lucid is a TensorFlow interpretability toolkit and visualization library designed to analyze the internal representations of neural networks. It functions as a gradient-based optimization framework that generates images and atlases to reveal the features learned by specific neurons and layers. The library enables the creation of activation atlases and the mapping of high-dimensional neural activations into lower-dimensional spaces to study model behavior. It utilizes differentiable image parametrization to optimize visual inputs that maximally activate network components. The system covers a

⬛ Python Individual Conditional Expectation Plot Toolbox

TreeInterpreter

Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).

This project is an agnostic model interpretability framework and explainability tool designed to provide local interpretable explanations for individual predictions. It functions as a local surrogate model that approximates the behavior of any machine learning classifier or regression model to identify the most influential features for a specific instance. The framework is designed to be model-agnostic, meaning it can explain predictions across tabular, text, and image data regardless of the underlying architecture. It employs local linear approximations and feature importance visualization t

python partial dependence plot toolbox

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations

Bias Auditing & Fair ML Toolkit

title: 'DIANNA: Deep Insight And Neural Network Analysis' tags: - Python - explainable AI - deep neural networks - ONNX - benchmark sets authors: - name: Elena Ranguelova^co-first author # note this makes a footnote saying 'co-first author' orcid: 0000-0002-9834-1756 affiliation: 1 - name:…

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🦊 Xplique (pronounced \ɛks.plik\ ) is a Python toolkit dedicated to explainability. The goal of this library is to gather the state of the art of Explainable AI to help you understand your complex neural network models. Originally built for Tensorflow's model it also works for PyTorch models…

Explainable outlier/anomaly detection based on smart decision tree grouping, similar in spirit to the GritBot software developed by RuleQuest research. Written in C++ with interfaces for R and Python (additional Ruby wrapper can be found here). Supports columns of types numeric, categorical,…

For calculating global feature importance using Shapley values.

Interpretability and explainability of data and machine learning models

This repo contains the code for our talk "Demystifying the neural network black box". Slides are available on Speaker Deck. This code has not been maintained for over a year. It's archived on 2024-12-18.

tensorflow implementation of Grad-CAM (CNN visualization)

Project Page // Paper // No-code Web Demo // Colab Notebook

effector an eXplainable AI package for tabular data. It: