508 Open source projects that are alternatives of or similar to interpretable-ml

A curated list of awesome machine learning interpretability resources.

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

Fit interpretable models. Explain blackbox machine learning.

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

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

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

A library that implements fairness-aware machine learning algorithms

Model interpretability and understanding for PyTorch

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

Data generator for Arena - interactive XAI dashboard

H2O.ai Machine Learning Interpretability Resources

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).

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

A Julia package for interpretable machine learning with stochastic Shapley values

Fast approximate Shapley values in R

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

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

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

Making decision trees competitive with neural networks on CIFAR10, CIFAR100, TinyImagenet200, Imagenet

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

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

Concept Bottleneck Models, ICML 2020

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4,5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting (GBDT, GBRT, GBM), Random Forest and Adaboost w/categorical features support for Python

A Python package implementing a new machine learning model for text classification with visualization tools for Explainable AI

🍊 📊 💡 Orange: Interactive data analysis

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

Material from "Random Forests and Gradient Boosting Machines in R" presented at Machine Learning Day '18

Plugin to make WordPress more Wiki-like by allowing more than one person to edit the same Post, Page, or Custom Post Type at the same time. When there are conflicting edits, it helps users to view, compare, and merge changes before saving.

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Exploring data and analyzing metrics for user-specific Medium Stats

🎓Repository for university labs on FAMCS, BSU

Information Inference Service of the OpenAIRE system

Go Scoring API for PMML

💹 SIMON is powerful, flexible, open-source and easy to use machine learning knowledge discovery platform 💻

Global-Local Capsule Network (GLCapsNet) is a capsule-based architecture able to provide context-based eye fixation prediction for several autonomous driving scenarios, while offering interpretability both globally and locally.

A curated list of (private) businesses publicly sharing their expenses.

Molecular Explanation Generator

A complete one stop solution for all the problems of Rural area people. 👩🏻‍🌾

o diário oficial do século XXI

Resources for the Data Mining for Bussiness and Governance course.

Moore Machine Networks (MMN): Learning Finite-State Representations of Recurrent Policy Networks

A lightweight library for designing and executing workflows in .NET Core

Data Exploration with Apache Drill

sciblox - Easier Data Science and Machine Learning

This repo consists of the projects that I completed as a part of the Udacity's Data Analyst Nanodegree's curriculum.

[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.

🌳 Stacked Gradient Boosting Machines

Official Implementation of Remembering for the Right Reasons (ICLR 2021)

Python Implementation of Decay Replay Mining (DREAM)

Kali Intelligence Suite (KIS) shall aid in the fast, autonomous, central, and comprehensive collection of intelligence by executing standard penetration testing tools. The collected data is internally stored in a structured manner to allow the fast identification and visualisation of the collected information.

java math expression parser is faster than JEP

Converts website to json using jQuery selectors

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

A place to store some examples which use Trillian APIs to build things.

An Interactive Approach to Understanding Supervised Learning Algorithms

Python implementations of the various methods used in FIRE 2019 conference.

Official compliant tool suite for implementing the Transparency and Consent Framework (TCF) v2.0. The essential toolkit for CMPs.

A Python implementation of divisive and hierarchical clustering algorithms. The algorithms were tested on the Human Gene DNA Sequence dataset and dendrograms were plotted.