Projects sharing features with Integrated Gradients

XAI - An eXplainability toolbox for machine learning

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

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

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

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

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

TreeInterpreter

Captum is an open-source library for explaining model predictions by attributing them to input features, neurons, and layers using gradient-based and perturbation-based methods. It provides a modular framework for implementing, evaluating, and combining a range of explanation techniques, including gradient-based attribution, perturbation-based analysis, game-theoretic Shapley value approximation, and surrogate model explanations, with support for parallelization and noise stabilization. The library distinguishes itself through its breadth of attribution methods and its support for advanced in

A toolbox to iNNvestigate neural networks' predictions!

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:…

tensorflow implementation of Grad-CAM (CNN visualization)

🦊 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,…

.. image:: sphinx/source/images/GammaFacetLogoRGB_LB.svg

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.

effector an eXplainable AI package for tabular data. It:

TorchCAM: class activation explorer

Interpret Community SDK

NeuroX provide all the necessary tooling to perform Interpretation and Analysis of (Deep) Neural Networks centered around Probing. Specifically, the toolkit provides: