66 Open Source Explainable Ml Software Projects
Free and open source explainable ml code projects including engines, APIs, generators, and tools.
Tensorwatch 3202 ⭐
Debugging, monitoring and visualization for Python Machine Learning and Data Science
Awesome Machine Learning Interpretability 2447 ⭐
A curated list of awesome machine learning interpretability resources.
Scalaconsultants Aspect Based Sentiment Analysis 324 ⭐
💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)
Cxplain 99 ⭐
Causal Explanation (CXPlain) is a method for explaining the predictions of any machine-learning model.
Shapr 95 ⭐
Explaining the output of machine learning models with more accurately estimated Shapley values
Datascience_artificialintelligence_utils 175 ⭐
Examples of Data Science projects and Artificial Intelligence use cases
Ml Fairness Framework 60 ⭐
FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
Shap_fold 34 ⭐
(Explainable AI) - Learning Non-Monotonic Logic Programs From Statistical Models Using High-Utility Itemset Mining
Sagemaker Explaining Credit Decisions 76 ⭐
Amazon SageMaker Solution for explaining credit decisions.
Diabetes_use_case 22 ⭐
Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/
Xai Iml Sota 42 ⭐
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Hc_ml 20 ⭐
Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.
Awesome Adversarial Interpretable Machine Learning 121 ⭐
💡 Adversarial attacks on model explanations, and evaluation approaches
Dlime_experiments 17 ⭐
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).
Article Information 2019 13 ⭐
Article for Special Edition of Information: Machine Learning with Python
Explainx 280 ⭐
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code.
Awesome Explainable Graph Reasoning 1759 ⭐
A collection of research papers and software related to explainability in graph machine learning.
Responsible Ai Widgets 399 ⭐
This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.
Interpretability Implementations Demos 438 ⭐
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
Carla Recourse Carla 149 ⭐
CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms
Shapley 134 ⭐
The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).
Whyisyoung Cade 60 ⭐
Code for our USENIX Security 2021 paper -- CADE: Detecting and Explaining Concept Drift Samples for Security Applications
Seggradcam 42 ⭐
SEG-GRAD-CAM: Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping
Cnn Raccoon 30 ⭐
Create interactive dashboards for your Convolutional Neural Networks with a single line of code!
Prototree 27 ⭐
ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
Health Fact Checking 23 ⭐
Dataset and code for "Explainable Automated Fact-Checking for Public Health Claims" from EMNLP 2020.
Recourse 20 ⭐
Code to reproduce our paper on probabilistic algorithmic recourse: https://arxiv.org/abs/2006.06831
Global Attribution Mapping 18 ⭐
GAM (Global Attribution Mapping) explains the landscape of neural network predictions across subpopulations
Cf Feasibility 16 ⭐
Code accompanying the paper "Preserving Causal Constraints in Counterfactual Explanations for Machine Learning Classifiers"
Lernd 13 ⭐
Lernd is ∂ILP (dILP) framework implementation based on Deepmind's paper Learning Explanatory Rules from Noisy Data.
Rseslib 11 ⭐
Rough set and machine learning data structures, algorithms and tools in Java, including algorithms for discernibility matrix, reducts, decision rules, discretization and classification, and tools: QMAK - for interactive machine learning and explainable ML, and Simple Grid Manager - for distributed experiments.
Rrl 34 ⭐
The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification".
Acv00 46 ⭐
ACV is a python library that provides explanations for any machine learning model or data. It gives local rule-based explanations for any model or data and different Shapley Values for tree-based models.
Mllp 14 ⭐
The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".
Deep_xf 27 ⭐
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.
Manikyabard Dashai 10 ⭐
DashAI provides a simple graphical user interface (GUI) that guides users through a step-by-step process through creating, training, and saving a model.