Trusted-AI/AIX360

Interpretability and explainability of data and machine learning models

Language: Python - Size: 358 MB - Last synced at: about 18 hours ago - Pushed at: 2 months ago - Stars: 1,687 - Forks: 312

interpretml/interpret

Fit interpretable models. Explain blackbox machine learning.

Language: C++ - Size: 14.7 MB - Last synced at: 18 minutes ago - Pushed at: 17 days ago - Stars: 6,486 - Forks: 746

EthicalML/xai

XAI - An eXplainability toolbox for machine learning

Language: Python - Size: 17.8 MB - Last synced at: 2 days ago - Pushed at: over 3 years ago - Stars: 1,168 - Forks: 179

microsoft/responsible-ai-toolbox

Responsible AI Toolbox is a suite of tools providing model and data exploration and assessment user interfaces and libraries that enable a better understanding of AI systems. These interfaces and libraries empower developers and stakeholders of AI systems to develop and monitor AI more responsibly, and take better data-driven actions.

Language: TypeScript - Size: 111 MB - Last synced at: 4 days ago - Pushed at: 3 months ago - Stars: 1,529 - Forks: 402

truera/trulens

Evaluation and Tracking for LLM Experiments

Language: Python - Size: 334 MB - Last synced at: 4 days ago - Pushed at: 4 days ago - Stars: 2,483 - Forks: 215

ModelOriented/DALEX

moDel Agnostic Language for Exploration and eXplanation

Language: Python - Size: 798 MB - Last synced at: 2 days ago - Pushed at: 3 months ago - Stars: 1,420 - Forks: 168

MAIF/shapash

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

Language: Jupyter Notebook - Size: 61.7 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 2,869 - Forks: 347

deel-ai/xplique

👋 Xplique is a Neural Networks Explainability Toolbox

Language: Python - Size: 33.4 MB - Last synced at: 6 days ago - Pushed at: 7 months ago - Stars: 688 - Forks: 58

fixouttech/fixout

Algorithmic inspection for trustworthy ML models

Language: Python - Size: 10.7 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 3 - Forks: 0

bgreenwell/ebm

Explainable Boosting Machines

Language: R - Size: 44.5 MB - Last synced at: 4 days ago - Pushed at: 2 months ago - Stars: 3 - Forks: 1

koo-ec/Awesome-LLM-Explainability

A curated list of explainability-related papers, articles, and resources focused on Large Language Models (LLMs). This repository aims to provide researchers, practitioners, and enthusiasts with insights into the explainability implications, challenges, and advancements surrounding these powerful models.

Size: 715 KB - Last synced at: 9 days ago - Pushed at: 9 days ago - Stars: 28 - Forks: 1

ashutosh1919/explainable-cnn

📦 PyTorch based visualization package for generating layer-wise explanations for CNNs.

Language: Python - Size: 3.99 MB - Last synced at: 8 days ago - Pushed at: over 1 year ago - Stars: 229 - Forks: 34

awslabs/sagemaker-explaining-credit-decisions

Amazon SageMaker Solution for explaining credit decisions.

Language: Python - Size: 2.77 MB - Last synced at: 2 days ago - Pushed at: about 2 years ago - Stars: 97 - Forks: 28

jacobgil/pytorch-grad-cam

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

Language: Python - Size: 134 MB - Last synced at: 10 days ago - Pushed at: about 1 month ago - Stars: 11,567 - Forks: 1,630

JHoelli/Awesome-Time-Series-Explainability

A list of (post-hoc) XAI for time series

Size: 424 KB - Last synced at: 10 days ago - Pushed at: 8 months ago - Stars: 134 - Forks: 16

divelab/DIG

A library for graph deep learning research

Language: Python - Size: 264 MB - Last synced at: 6 days ago - Pushed at: 10 months ago - Stars: 1,952 - Forks: 286

NorskRegnesentral/shapr

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

Language: R - Size: 104 MB - Last synced at: 12 days ago - Pushed at: 13 days ago - Stars: 158 - Forks: 36

interpretml/DiCE

Generate Diverse Counterfactual Explanations for any machine learning model.

Language: Python - Size: 15.2 MB - Last synced at: 13 days ago - Pushed at: 6 months ago - Stars: 1,397 - Forks: 200

csinva/imodels

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

Language: Jupyter Notebook - Size: 162 MB - Last synced at: 13 days ago - Pushed at: 2 months ago - Stars: 1,449 - Forks: 124

salesforce/OmniXAI

OmniXAI: A Library for eXplainable AI

Language: Jupyter Notebook - Size: 64.7 MB - Last synced at: 13 days ago - Pushed at: 10 months ago - Stars: 916 - Forks: 100

microsoft/tensorwatch

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

Language: Jupyter Notebook - Size: 21.2 MB - Last synced at: 4 days ago - Pushed at: over 1 year ago - Stars: 3,440 - Forks: 361

maxreiss123/GeneExpressionProgramming.jl

Gene Expression Programming for symbolic regression in Julia

Language: Julia - Size: 3.6 MB - Last synced at: 15 days ago - Pushed at: 16 days ago - Stars: 1 - Forks: 0

AstraZeneca/awesome-shapley-value

Reading list for "The Shapley Value in Machine Learning" (JCAI 2022)

Size: 622 KB - Last synced at: 7 days ago - Pushed at: almost 3 years ago - Stars: 150 - Forks: 12

BirkhoffG/Explainable-ML-Papers

A list of research papers of explainable machine learning.

Size: 13.7 KB - Last synced at: 4 days ago - Pushed at: almost 4 years ago - Stars: 36 - Forks: 3

MarcelRobeer/explabox

Explore/examine/explain/expose your model with the explabox!

Language: Python - Size: 3.03 MB - Last synced at: 17 days ago - Pushed at: 17 days ago - Stars: 16 - Forks: 0

M-Nauta/ProtoTree

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

Language: Python - Size: 870 KB - Last synced at: 3 days ago - Pushed at: almost 3 years ago - Stars: 101 - Forks: 21

jphall663/awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

Size: 4.45 MB - Last synced at: 17 days ago - Pushed at: 24 days ago - Stars: 3,770 - Forks: 599

capitalone/global-attribution-mapping

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

Language: Python - Size: 3.64 MB - Last synced at: 18 days ago - Pushed at: 18 days ago - Stars: 33 - Forks: 25

Networks-Learning/strategic-decisions

Code and data for decision making under strategic behavior, NeurIPS 2020 & Management Science 2024.

Language: Jupyter Notebook - Size: 5.94 MB - Last synced at: 18 days ago - Pushed at: about 1 year ago - Stars: 29 - Forks: 5

fzi-forschungszentrum-informatik/TSInterpret

An Open-Source Library for the interpretability of time series classifiers

Language: Python - Size: 200 MB - Last synced at: 9 days ago - Pushed at: 6 months ago - Stars: 133 - Forks: 14

ajayarunachalam/Deep_XF

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.

Language: Jupyter Notebook - Size: 27.5 MB - Last synced at: 5 days ago - Pushed at: over 2 years ago - Stars: 118 - Forks: 24

AstraZeneca/awesome-explainable-graph-reasoning

A collection of research papers and software related to explainability in graph machine learning.

Size: 1.7 MB - Last synced at: 13 days ago - Pushed at: about 3 years ago - Stars: 1,961 - Forks: 130

carla-recourse/CARLA

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

Language: Python - Size: 1.89 MB - Last synced at: 4 days ago - Pushed at: over 1 year ago - Stars: 288 - Forks: 64

microsoft/responsible-ai-workshop

Responsible AI Workshop: a series of tutorials & walkthroughs to illustrate how put responsible AI into practice

Language: Jupyter Notebook - Size: 237 MB - Last synced at: 4 days ago - Pushed at: 2 months ago - Stars: 44 - Forks: 9

vojtech-balek/llm-features

Repository for code backing the article "LLM-based feature generation from text for interpretable machine learning".

Language: Jupyter Notebook - Size: 90.1 MB - Last synced at: 23 days ago - Pushed at: 23 days ago - Stars: 0 - Forks: 1

ottenbreit-data-science/aplr

APLR builds predictive, interpretable regression and classification models using Automatic Piecewise Linear Regression. It often rivals tree-based methods in predictive accuracy while offering smoother and interpretable predictions.

Language: C++ - Size: 6.13 MB - Last synced at: 24 days ago - Pushed at: 25 days ago - Stars: 17 - Forks: 4

benedekrozemberczki/shapley

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

Language: Python - Size: 826 KB - Last synced at: 6 days ago - Pushed at: almost 2 years ago - Stars: 220 - Forks: 35

awojna/Rseslib

Data structures, algorithms and tools for rough sets, machine learning and data mining, including algorithms for discernibility matrix, reducts, decision rules, classification (KNN, NeuralNet, AQ15, RoughSet, RIONIDA, SVM, C4.5, and many others), discretization (1R, EntropyMinimization, ChiMerge, MD), and tool for explainable and interactive ML.

Language: Java - Size: 1.72 MB - Last synced at: 27 days ago - Pushed at: 27 days ago - Stars: 16 - Forks: 5

ritu-thombre99/explaining_quanvolution

This work explores whether the quanvolution neural network is explainable by proposing a novel mathematical approach for quantifying explainability

Language: Jupyter Notebook - Size: 840 MB - Last synced at: 27 days ago - Pushed at: 27 days ago - Stars: 0 - Forks: 0

wilsonjr/ClusterShapley

Explaining dimensionality results using SHAP values

Language: C++ - Size: 655 KB - Last synced at: 10 days ago - Pushed at: 4 months ago - Stars: 54 - Forks: 6

ScalaConsultants/Aspect-Based-Sentiment-Analysis

💭 Aspect-Based-Sentiment-Analysis: Transformer & Explainable ML (TensorFlow)

Language: Python - Size: 1.8 MB - Last synced at: 13 days ago - Pushed at: about 2 years ago - Stars: 568 - Forks: 90

h-fuzzy-logic/explainability-fairness-safety-for-ai

Resources to improve the explainability, fairness, and safety of your AI

Size: 9.44 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 3 - Forks: 1

explainX/explainx

Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ [email protected]

Language: Jupyter Notebook - Size: 61.3 MB - Last synced at: 29 days ago - Pushed at: 9 months ago - Stars: 430 - Forks: 56

ModelOriented/survex

Explainable Machine Learning in Survival Analysis

Language: R - Size: 309 MB - Last synced at: 7 days ago - Pushed at: 11 months ago - Stars: 111 - Forks: 10

h-fuzzy-logic/hello-penguins

Machine learning experiments with the Palmer Penguins dataset

Language: Python - Size: 1.36 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 0 - Forks: 0

KaryFramling/py-ciu

Explainable Artificial Intelligence through Contextual Importance and Utility

Language: Jupyter Notebook - Size: 27.4 MB - Last synced at: 29 days ago - Pushed at: 9 months ago - Stars: 28 - Forks: 8

daikikatsuragawa/awesome-counterfactual-explanations

This repository is a curated collection of information (keywords, papers, libraries, books, etc.) about counterfactual explanations🙃 Contributions are welcome! Our maintenance capacity is limited, so we highly appreciate pull requests.

Size: 12.7 KB - Last synced at: 6 days ago - Pushed at: over 2 years ago - Stars: 18 - Forks: 0

henrikbostrom/xrf

xrf is a Python package that implements random forests with example attribution

Language: Python - Size: 360 KB - Last synced at: 1 day ago - Pushed at: 5 months ago - Stars: 3 - Forks: 0

sametcopur/treemind

treemind interprets ensemble tree models by analyzing individual trees and their predictions, providing insights into the decision-making process.

Language: Python - Size: 373 MB - Last synced at: 16 days ago - Pushed at: 4 months ago - Stars: 22 - Forks: 2

neemakot/Health-Fact-Checking 📦

Dataset and code for "Explainable Automated Fact-Checking for Public Health Claims" from EMNLP 2020.

Language: Python - Size: 23.7 MB - Last synced at: 30 days ago - Pushed at: about 4 years ago - Stars: 60 - Forks: 10

keisen/tf-keras-vis

Neural network visualization toolkit for tf.keras

Language: Python - Size: 97.1 MB - Last synced at: 30 days ago - Pushed at: 2 months ago - Stars: 324 - Forks: 45

pyartemis/artemis

A Python package with explanation methods for extraction of feature interactions from predictive models

Language: Jupyter Notebook - Size: 16.6 MB - Last synced at: 6 days ago - Pushed at: over 1 year ago - Stars: 30 - Forks: 0

tpoisot/InterpretableSDMWithJulia

Slides for the "Interpretable SDM with Julia" workshop

Language: TeX - Size: 268 MB - Last synced at: 29 days ago - Pushed at: 6 months ago - Stars: 5 - Forks: 2

kaspersgit/ml_2_sql

Automating machine learning training and save an SQL version of the model

Language: Python - Size: 17.4 MB - Last synced at: 14 days ago - Pushed at: about 2 months ago - Stars: 8 - Forks: 2

danyvarghese/PyGol

A novel Inductive Logic Programming(ILP) system based on Meta Inverse Entailment in Python.

Language: C - Size: 6.9 MB - Last synced at: about 2 months ago - Pushed at: about 2 months ago - Stars: 13 - Forks: 3

salimamoukou/acv00

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.

Language: Jupyter Notebook - Size: 19 MB - Last synced at: 29 days ago - Pushed at: over 2 years ago - Stars: 100 - Forks: 11

mpolinowski/sklearn-model-explainability

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model.

Language: Jupyter Notebook - Size: 1.37 MB - Last synced at: about 2 months ago - Pushed at: over 1 year ago - Stars: 1 - Forks: 0

rikhuijzer/SIRUS.jl

Interpretable Machine Learning via Rule Extraction

Language: Julia - Size: 1.35 MB - Last synced at: 9 days ago - Pushed at: 7 months ago - Stars: 36 - Forks: 2

csinva/clinical-rule-development

Building and vetting clinical decision rules.

Language: Jupyter Notebook - Size: 160 MB - Last synced at: 4 days ago - Pushed at: about 2 months ago - Stars: 8 - Forks: 2

UTS-CASLab/hyperbox-brain

A scikit-learn compatible hyperbox-based machine learning library in Python

Language: Python - Size: 8.45 MB - Last synced at: 30 days ago - Pushed at: 4 months ago - Stars: 33 - Forks: 2

zalkikar/mlm-bias

Measuring Biases in Masked Language Models for PyTorch Transformers. Support for multiple social biases and evaluation measures.

Language: Python - Size: 45.9 KB - Last synced at: 28 days ago - Pushed at: 5 months ago - Stars: 4 - Forks: 1

si-cim/cbc-aaai-2025

Deep CBC Models for Prototype Based Interpretability Benchmarks

Language: Python - Size: 1.65 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 0 - Forks: 0

bifold-pathomics/xMIL

xMIL: Insightful Explanations for Multiple Instance Learning in Histopathology

Language: Jupyter Notebook - Size: 29.3 MB - Last synced at: 2 months ago - Pushed at: 2 months ago - Stars: 12 - Forks: 2

firmai/ml-fairness-framework

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

Language: Jupyter Notebook - Size: 10.5 MB - Last synced at: 5 days ago - Pushed at: over 3 years ago - Stars: 71 - Forks: 16

AI-vidence/antakia

AntakIA is THE tool to explain an ML model or replace it with a collection of basic explainable models.

Language: Python - Size: 188 MB - Last synced at: 16 days ago - Pushed at: 10 months ago - Stars: 13 - Forks: 0

dylan-slack/TalkToModel

TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!

Language: Python - Size: 10.1 MB - Last synced at: 29 days ago - Pushed at: almost 2 years ago - Stars: 120 - Forks: 25

bgreenwell/fastshap

Fast approximate Shapley values in R

Language: R - Size: 99.4 MB - Last synced at: about 1 month ago - Pushed at: about 1 year ago - Stars: 119 - Forks: 18

turab45/Explainable-Machine-Learning-for-Parkinson-s-Disease-Detection-Using-Speech--Biomarkers

Explainable Machine Learning for Parkinson’s Disease Detection Using Speech Biomarkers

Language: Jupyter Notebook - Size: 1.63 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

vojtech-balek/SMERVisual

Package implementing the SMER method for explainable machine learning on image data.

Language: Python - Size: 95.7 KB - Last synced at: 13 days ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

LamineTourelab/Explainable-AI

In this repository you will fine explainability of machine learning models.

Size: 8.79 KB - Last synced at: 3 months ago - Pushed at: about 2 years ago - Stars: 7 - Forks: 0

giobbu/explainable-probabilistic-forecasting

Size: 1.15 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 0 - Forks: 0

adaamko/POTATO

XAI based human-in-the-loop framework for automatic rule-learning.

Language: Jupyter Notebook - Size: 6.07 MB - Last synced at: 9 days ago - Pushed at: 10 months ago - Stars: 48 - Forks: 8

donlapark/XLabel

XLabel: An Explainable Data Labeling Assistant

Language: Jupyter Notebook - Size: 2.84 MB - Last synced at: 4 days ago - Pushed at: about 2 years ago - Stars: 7 - Forks: 2

manikyabard/DashAI

DashAI provides a simple graphical user interface (GUI) that guides users through a step-by-step process through creating, training, and saving a model.

Language: Python - Size: 390 MB - Last synced at: 29 days ago - Pushed at: over 2 years ago - Stars: 12 - Forks: 6

jphall663/diabetes_use_case

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

Language: Jupyter Notebook - Size: 73.7 MB - Last synced at: about 1 month ago - Pushed at: 11 months ago - Stars: 27 - Forks: 13

ModelOriented/fairmodels

Flexible tool for bias detection, visualization, and mitigation

Language: R - Size: 142 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 86 - Forks: 17

gianluigilopardo/smace

Code for the paper "SMACE: A New Method for the Interpretability of Composite Decision Systems", ECML 2022

Language: Jupyter Notebook - Size: 806 KB - Last synced at: 7 days ago - Pushed at: about 2 years ago - Stars: 10 - Forks: 1

mmaisonnave/unplanned-hospital-readmission-prediction

Explainable ML applied to healthcare data from Nova Scotia (Canada) to identify patients at risk of unplanned hospital readmission.

Language: Python - Size: 532 MB - Last synced at: 3 months ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

rikardvinge/explainpolysvm

ExplainPolySVM is a python package to provide interpretation and explainability to Support Vector Machine models trained with polynomial kernels. The package can be used with any SVM model as long as the components of the model can be extracted.

Language: Python - Size: 28.7 MB - Last synced at: 7 days ago - Pushed at: 3 months ago - Stars: 1 - Forks: 0

utwente-dmb/xai-papers

Language: TypeScript - Size: 147 MB - Last synced at: about 8 hours ago - Pushed at: 9 months ago - Stars: 17 - Forks: 9

Optum/long-medical-document-lms

Explain and train language models that extract information from long medical documents with the Masked Sampling Procedure (MSP)

Language: Jupyter Notebook - Size: 27 MB - Last synced at: 27 days ago - Pushed at: 9 months ago - Stars: 7 - Forks: 0

ieddeveci/featureVisualization_activationMaximization

Feature Visualization of Deep Neural Networks, Term Project, MMI727 Deep Learning: Methods and Applications course, METU.

Language: Jupyter Notebook - Size: 18.6 KB - Last synced at: 4 months ago - Pushed at: 4 months ago - Stars: 0 - Forks: 0

pyladiesams/intro-to-explainabilty-in-finance-oct2024

Building a model is just one piece of the puzzle in data science; explaining how it works is just as important, especially in finance where transparency and explainability is key.

Language: Jupyter Notebook - Size: 7.48 MB - Last synced at: about 1 month ago - Pushed at: 7 months ago - Stars: 2 - Forks: 2

willbakst/pytorch-lattice

A PyTorch implementation of constrained optimization and modeling techniques

Language: Python - Size: 1.19 MB - Last synced at: about 22 hours ago - Pushed at: about 1 year ago - Stars: 30 - Forks: 1

jphall663/jsm_2018_paper

Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlineprogram/AbstractDetails.cfm?abstractid=329539

Language: TeX - Size: 12.7 MB - Last synced at: about 1 month ago - Pushed at: over 6 years ago - Stars: 9 - Forks: 2

OmidGhadami95/EfficientNetV2_CatVSDog

Binary classification, SHAP (Explainable Artificial Intelligence), and Grid Search (for tuning hyperparameters) using EfficientNetV2-B0 on Cat VS Dog dataset.

Language: Jupyter Notebook - Size: 1.54 MB - Last synced at: 3 months ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

EloiZ/awesome-contrastive-explanation

A curated list of awesome contrastive explanation in ML resources

Size: 18.6 KB - Last synced at: 5 days ago - Pushed at: about 5 years ago - Stars: 4 - Forks: 0

markusorsi/mapchiral

Chiral version of the MinHashed Atom-Pair Fingerprint

Language: Python - Size: 323 KB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 19 - Forks: 5

pterhoer/ExplainableFaceImageQuality

Pixel-Level Face Image Quality Assessment for Explainable Face Recognition

Language: Python - Size: 1.87 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 53 - Forks: 9

piotromashov/baycon

Research project on generation of counterfactuals for eXplainable AI, based on Bayesian Generation

Language: Python - Size: 4.37 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 8 - Forks: 0

LukePower01/ml-to-qml

Final year project, exploring the field of quantum machine learning.

Language: Jupyter Notebook - Size: 166 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 1 - Forks: 1

SonghuaHu-UMD/Explainable_AI_Comparison

Language: Python - Size: 16.7 MB - Last synced at: 6 days ago - Pushed at: over 2 years ago - Stars: 6 - Forks: 3

tomjanus/ghg_emissions_myanmar

Collection of notebooks accompanying a research paper on evaluating GHG emissions from hydroelectric, multipurpose and irrigation reservoirs in Myanmar

Language: Jupyter Notebook - Size: 3.67 MB - Last synced at: 5 months ago - Pushed at: 5 months ago - Stars: 2 - Forks: 0

fork123aniket/Model-agnostic-Graph-Explainability-from-Scratch

Implementation of Model-Agnostic Graph Explainability Technique from Scratch in PyTorch

Language: Python - Size: 3.08 MB - Last synced at: 2 months ago - Pushed at: about 2 years ago - Stars: 2 - Forks: 0

forestry-labs/distillML

An R package providing functions for interpreting and distilling machine learning models

Language: R - Size: 9.76 MB - Last synced at: 19 days ago - Pushed at: about 2 years ago - Stars: 7 - Forks: 2

Pranav2092/Intrustion-Detection-Using-Modified-Tree-SHAP

Language: Jupyter Notebook - Size: 118 KB - Last synced at: about 1 month ago - Pushed at: 6 months ago - Stars: 1 - Forks: 0

marvinbuss/ExplainableML-Vision

This repository introduces different Explainable AI approaches and demonstrates how they can be implemented with PyTorch and torchvision. Used approaches are Class Activation Mappings, LIMA and SHapley Additive exPlanations.

Language: Jupyter Notebook - Size: 52.4 MB - Last synced at: about 1 month ago - Pushed at: almost 3 years ago - Stars: 30 - Forks: 5

BirkhoffG/explainax 📦

JAX-based Model Explanation and Interpretation Library

Language: Jupyter Notebook - Size: 439 KB - Last synced at: 20 days ago - Pushed at: about 1 year ago - Stars: 1 - Forks: 0

NREL/BUTTER-Clarifier

This repository contains a python package of neural network interpretability and explainablility methods, focusing on the latent space, that can be easily integrated into a keras training routine using a callback to compute and capture outputs of these methods during training.

Language: Python - Size: 15.6 KB - Last synced at: 5 months ago - Pushed at: 9 months ago - Stars: 1 - Forks: 0

wangyongjie-ntu/CFAI

A collection of algorithms of counterfactual explanations.

Language: Python - Size: 8.97 MB - Last synced at: 13 days ago - Pushed at: about 4 years ago - Stars: 50 - Forks: 9