1515 Open source projects that are alternatives of or similar to Interpret

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

A curated list of awesome machine learning interpretability resources.

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

Data generator for Arena - interactive XAI dashboard

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

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Fast approximate Shapley values in R

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

Papers about explainability of GNNs

In this part, I've introduced and experimented with ways to interpret and evaluate models in the field of image. (Pytorch)

XAI - An eXplainability toolbox for machine learning

Model interpretability and understanding for PyTorch

Predictive AI layer for existing databases.

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.

Debugging, monitoring and visualization for Python Machine Learning and Data Science

A game theoretic approach to explain the output of any machine learning model.

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

[UNMAINTAINED] Automated machine learning for analytics & production

Collection of NLP model explanations and accompanying analysis tools

Visualization tool for Graph Neural Networks

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

ExpMRC: Explainability Evaluation for Machine Reading Comprehension

MindsDB server allows you to consume and expose MindsDB workflows, through http.

Examples of Data Science projects and Artificial Intelligence use cases

Deep Learning Computer Vision Algorithms for Real-World Use

A Smart, Automatic, Fast and Lightweight Web Scraper for Python

Molecular Explanation Generator

Concept Bottleneck Models, ICML 2020

Model explainability that works seamlessly with 🤗 transformers. Explain your transformers model in just 2 lines of code.

도서 "핸즈온 머신러닝"의 예제와 연습문제를 담은 주피터 노트북입니다.

🌓 Bringing pjreddie's DarkNet out of the shadows #yolo

🤖 Chatbot Framework for Node.js.

Code for NeurIPS 2019 paper ``Self-Critical Reasoning for Robust Visual Question Answering''

Package towards building Explainable Forecasting and Nowcasting Models with State-of-the-art Deep Neural Networks and Dynamic Factor Model on Time Series data sets with single line of code. Also, provides utilify facility for time-series signal similarities matching, and removing noise from timeseries signals.

A Julia package for interpretable machine learning with stochastic Shapley values

Multiple implementations for abstractive text summurization , using google colab

Command Line Artificial Intelligence or CLAI is an open-sourced project from IBM Research aimed to bring the power of AI to the command line interface.

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models.

For calculating global feature importance using Shapley values.

Explaining the output of machine learning models with more accurately estimated Shapley values

GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations

(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining

A lightweight implementation of removal-based explanations for ML models.

Applying Machine Learning and AI Algorithms applied to Trading for better performance and low Std.

LDA topic modeling for node.js

《深度学习》花书手推笔记

AI Automated Planning with STRIPS and PDDL in Node.js

Machine Learning Platform for Kubernetes (MLOps tools for experimentation and automation)