interpretable-machine-learning | Topic

Topic: "interpretable-machine-learning"

interpretml/interpret

Fit interpretable models. Explain blackbox machine learning.

Language: C++ - Size: 14.9 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 6,527 - Forks: 750

jphall663/awesome-machine-learning-interpretability

A curated list of awesome responsible machine learning resources.

Size: 3.2 MB - Last synced at: 2 days ago - Pushed at: 2 days ago - Stars: 3,805 - Forks: 604

ModelOriented/DALEX

moDel Agnostic Language for Exploration and eXplanation

Language: Python - Size: 798 MB - Last synced at: about 1 month ago - Pushed at: 4 months ago - Stars: 1,422 - Forks: 168

interpretml/DiCE

Generate Diverse Counterfactual Explanations for any machine learning model.

Language: Python - Size: 14.9 MB - Last synced at: 23 days ago - Pushed at: 23 days ago - Stars: 1,405 - Forks: 203

SelfExplainML/PiML-Toolbox

PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics

Language: Jupyter Notebook - Size: 250 MB - Last synced at: 7 days ago - Pushed at: 3 months ago - Stars: 1,253 - Forks: 130

salesforce/OmniXAI

OmniXAI: A Library for eXplainable AI

Language: Jupyter Notebook - Size: 64.7 MB - Last synced at: 25 days ago - Pushed at: 11 months ago - Stars: 924 - Forks: 99

lopusz/awesome-interpretable-machine-learning

Language: Python - Size: 1.47 MB - Last synced at: about 2 months ago - Pushed at: over 2 years ago - Stars: 916 - Forks: 139

dswah/pyGAM

[HELP REQUESTED] Generalized Additive Models in Python

Language: Python - Size: 15.6 MB - Last synced at: about 1 year ago - Pushed at: about 1 year ago - Stars: 839 - Forks: 154

pbiecek/xai_resources

Interesting resources related to XAI (Explainable Artificial Intelligence)

Language: R - Size: 13.2 MB - Last synced at: about 1 month ago - Pushed at: about 3 years ago - Stars: 830 - Forks: 138

jphall663/interpretable_machine_learning_with_python

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

Language: Jupyter Notebook - Size: 34.7 MB - Last synced at: about 1 month ago - Pushed at: about 1 year ago - Stars: 679 - Forks: 207

mmschlk/shapiq

Shapley Interactions and Shapley Values for Machine Learning

Language: Python - Size: 312 MB - Last synced at: 6 days ago - Pushed at: 6 days ago - Stars: 560 - Forks: 36

h2oai/mli-resources

H2O.ai Machine Learning Interpretability Resources

Language: Jupyter Notebook - Size: 65.8 MB - Last synced at: 29 days ago - Pushed at: over 4 years ago - Stars: 488 - Forks: 130

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: about 1 month ago - Pushed at: 10 months ago - Stars: 436 - Forks: 56

sergioburdisso/pyss3

A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)

Language: Python - Size: 102 MB - Last synced at: 7 days ago - Pushed at: 17 days ago - Stars: 341 - Forks: 44

ModelOriented/modelStudio

📍 Interactive Studio for Explanatory Model Analysis

Language: R - Size: 36.2 MB - Last synced at: 8 days ago - Pushed at: almost 2 years ago - Stars: 332 - Forks: 32

hbaniecki/adversarial-explainable-ai

💡 Adversarial attacks on explanations and how to defend them

Size: 2.62 MB - Last synced at: 3 months ago - Pushed at: 7 months ago - Stars: 314 - Forks: 48

salesforce/ETSformer

PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting

Language: Python - Size: 459 KB - Last synced at: about 1 month ago - Pushed at: about 1 year ago - Stars: 278 - Forks: 43

idealo/cnn-exposed 📦

🕵️‍♂️ Interpreting Convolutional Neural Network (CNN) Results.

Language: Jupyter Notebook - Size: 69.6 MB - Last synced at: about 2 months ago - Pushed at: 6 months ago - Stars: 177 - Forks: 30

jrieke/cnn-interpretability

🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease

Language: Jupyter Notebook - Size: 61.5 MB - Last synced at: 3 days ago - Pushed at: almost 6 years ago - Stars: 171 - Forks: 50

Graph-COM/GSAT

[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.

Language: Jupyter Notebook - Size: 1.57 MB - Last synced at: 3 months ago - Pushed at: over 1 year ago - Stars: 167 - Forks: 22

pietrobarbiero/pytorch_explain

PyTorch Explain: Interpretable Deep Learning in Python.

Language: Jupyter Notebook - Size: 42.1 MB - Last synced at: about 1 month ago - Pushed at: about 1 year ago - Stars: 154 - Forks: 14

JHoelli/Awesome-Time-Series-Explainability

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

Size: 424 KB - Last synced at: 3 days ago - Pushed at: 10 months ago - Stars: 147 - Forks: 18

fzi-forschungszentrum-informatik/TSInterpret

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

Language: Python - Size: 200 MB - Last synced at: 8 days ago - Pushed at: 7 months ago - Stars: 134 - Forks: 15

yewsiang/ConceptBottleneck

Concept Bottleneck Models, ICML 2020

Language: Python - Size: 1.51 MB - Last synced at: over 1 year ago - Pushed at: over 2 years ago - Stars: 132 - Forks: 23

rachtibat/zennit-crp

An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization

Language: Jupyter Notebook - Size: 17.9 MB - Last synced at: about 1 month ago - Pushed at: about 1 year ago - Stars: 127 - Forks: 18

bgreenwell/fastshap

Fast approximate Shapley values in R

Language: R - Size: 99.4 MB - Last synced at: about 1 month ago - Pushed at: over 1 year ago - Stars: 121 - Forks: 18

VincentGranville/Machine-Learning

Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.

Language: Python - Size: 52.1 MB - Last synced at: about 23 hours ago - Pushed at: about 23 hours ago - Stars: 120 - Forks: 35

dylan-slack/TalkToModel

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

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

ModelOriented/survex

Explainable Machine Learning in Survival Analysis

Language: R - Size: 309 MB - Last synced at: 26 days ago - Pushed at: about 1 year ago - Stars: 112 - Forks: 10

andreysharapov/xaience

All about explainable AI, algorithmic fairness and more

Language: HTML - Size: 7.81 GB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 106 - Forks: 12

M-Nauta/ProtoTree

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

Language: Python - Size: 870 KB - Last synced at: about 2 months ago - Pushed at: almost 3 years ago - Stars: 101 - Forks: 21

nredell/ShapML.jl

A Julia package for interpretable machine learning with stochastic Shapley values

Language: Julia - Size: 529 KB - Last synced at: 4 days ago - Pushed at: about 1 year ago - Stars: 90 - Forks: 8

ModelOriented/treeshap

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Language: R - Size: 19.7 MB - Last synced at: 21 days ago - Pushed at: 11 months ago - Stars: 88 - Forks: 24

charmlab/mace

Model Agnostic Counterfactual Explanations

Language: Python - Size: 3 MB - Last synced at: 5 months ago - Pushed at: over 2 years ago - Stars: 87 - Forks: 12

12wang3/rrl

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Language: Python - Size: 561 KB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 84 - Forks: 22

zju-vipa/awesome-neural-trees

Introduction, selected papers and possible corresponding codes in our review paper "A Survey of Neural Trees"

Size: 1.66 MB - Last synced at: about 2 months ago - Pushed at: over 2 years ago - Stars: 78 - Forks: 9

BioroboticsLab/IBA

Information Bottlenecks for Attribution

Language: Python - Size: 201 KB - Last synced at: 7 months ago - Pushed at: over 2 years ago - Stars: 75 - Forks: 9

inouye-lab/ShapleyExplanationNetworks

Implementation of the paper "Shapley Explanation Networks"

Language: Python - Size: 25.4 KB - Last synced at: almost 2 years ago - Pushed at: over 4 years ago - Stars: 75 - Forks: 18

SinaMohseni/Awesome-XAI-Evaluation

Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems

Size: 251 KB - Last synced at: 12 days ago - Pushed at: about 3 years ago - Stars: 74 - Forks: 10

nredell/shapFlex

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

Language: R - Size: 2.13 MB - Last synced at: 2 months ago - Pushed at: about 5 years ago - Stars: 74 - Forks: 7

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: 4 days ago - Pushed at: over 3 years ago - Stars: 71 - Forks: 16

ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book

Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications

Language: Jupyter Notebook - Size: 89.6 MB - Last synced at: about 2 years ago - Pushed at: about 2 years ago - Stars: 64 - Forks: 16

pillowsofwind/Knowledge-Conflicts-Survey

The official GitHub repo for the survey paper "Knowledge Conflicts for LLMs: A Survey"

Size: 1.25 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 53 - Forks: 0

cair/convolutional-tsetlin-machine-tutorial

Tutorial on the Convolutional Tsetlin Machine

Language: Python - Size: 316 KB - Last synced at: 2 months ago - Pushed at: over 4 years ago - Stars: 53 - Forks: 13

pbiecek/XAIatERUM2020

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

Language: R - Size: 64.5 MB - Last synced at: 2 months ago - Pushed at: almost 4 years ago - Stars: 52 - Forks: 11

rehmanzafar/xai-iml-sota

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

Language: R - Size: 1.12 MB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 51 - Forks: 13

RishiDarkDevil/daam-i2i Fork of castorini/daam

Diffusion attentive attribution maps for interpreting Stable Diffusion for image-to-image attention.

Language: Python - Size: 30.3 MB - Last synced at: 6 months ago - Pushed at: 6 months ago - Stars: 49 - Forks: 1

adaamko/POTATO

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

Language: Jupyter Notebook - Size: 6.07 MB - Last synced at: 20 days ago - Pushed at: 12 months ago - Stars: 49 - Forks: 8

ModelOriented/kernelshap

Different SHAP algorithms

Language: R - Size: 2.5 MB - Last synced at: about 2 months ago - Pushed at: 3 months ago - Stars: 47 - Forks: 7

gianlucatruda/quantified-sleep

Quantified Sleep: Machine learning techniques for observational n-of-1 studies.

Language: Jupyter Notebook - Size: 11.1 MB - Last synced at: about 2 months ago - Pushed at: about 4 years ago - Stars: 46 - Forks: 2

M-Nauta/PIPNet

PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)

Language: Python - Size: 3.85 MB - Last synced at: over 1 year ago - Pushed at: over 1 year ago - Stars: 43 - Forks: 8

cair/pyTsetlinMachineParallel

Multi-threaded implementation of the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features and multigranularity.

Language: C - Size: 290 KB - Last synced at: about 1 month ago - Pushed at: over 2 years ago - Stars: 41 - Forks: 9

keiserlab/plaquebox-paper

Repo for Tang et al, bioRxiv 454793 (2018)

Language: Jupyter Notebook - Size: 31.6 MB - Last synced at: about 1 month ago - Pushed at: over 6 years ago - Stars: 41 - Forks: 25

sayakpaul/Benchmarking-and-MLI-experiments-on-the-Adult-dataset

Contains benchmarking and interpretability experiments on the Adult dataset using several libraries

Language: Jupyter Notebook - Size: 869 KB - Last synced at: 14 days ago - Pushed at: about 6 years ago - Stars: 35 - Forks: 12

pkassraie/CNN_Visualizations

Visualization of Adversarial Examples

Language: Python - Size: 260 MB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 31 - Forks: 4

IBM/AutoPeptideML

AutoML system for building trustworthy peptide bioactivity predictors

Language: Python - Size: 48.9 MB - Last synced at: 7 days ago - Pushed at: 7 days ago - Stars: 30 - Forks: 2

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: 18 days ago - Pushed at: over 1 year ago - Stars: 30 - Forks: 0

Samyu0304/graph-information-bottleneck-for-Subgraph-Recognition

Language: Python - Size: 1.94 MB - Last synced at: over 2 years ago - Pushed at: over 2 years ago - Stars: 28 - Forks: 4

gully/blase

Interpretable Machine Learning for astronomical spectroscopy in PyTorch and JAX

Language: Jupyter Notebook - Size: 74.3 MB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 27 - Forks: 8

FarnoushRJ/MambaLRP

[NeurIPS 2024] Official implementation of the paper "MambaLRP: Explaining Selective State Space Sequence Models".

Language: Python - Size: 7.5 MB - Last synced at: 8 months ago - Pushed at: 8 months ago - Stars: 27 - Forks: 3

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: 3 months ago - Pushed at: about 1 year ago - Stars: 27 - Forks: 13

solegalli/machine-learning-interpretability

Code repository for the online course Machine Learning Interpretability

Language: Jupyter Notebook - Size: 23.9 MB - Last synced at: 2 months ago - Pushed at: 8 months ago - Stars: 26 - Forks: 18

deepfx/netlens

A toolkit for interpreting and analyzing neural networks (vision)

Language: Jupyter Notebook - Size: 130 MB - Last synced at: 4 days ago - Pushed at: almost 5 years ago - Stars: 26 - Forks: 2

YujiaBao/R2A

"Deriving Machine Attention from Human Rationales" EMNLP 2018

Language: Python - Size: 1.42 MB - Last synced at: over 1 year ago - Pushed at: over 6 years ago - Stars: 26 - Forks: 5

Crisp-Unimib/ContrXT

a tool for comparing the predictions of any text classifiers

Language: Python - Size: 6.4 MB - Last synced at: 28 days ago - Pushed at: almost 3 years ago - Stars: 25 - Forks: 2

imatge-upc/SurvLIMEpy

Local interpretability for survival models

Language: Python - Size: 3.7 MB - Last synced at: 5 days ago - Pushed at: about 1 year ago - Stars: 24 - Forks: 4

tlverse/causalglm

Interpretable and model-robust causal inference for heterogeneous treatment effects using generalized linear working models with targeted machine-learning

Language: R - Size: 7.27 MB - Last synced at: 3 months ago - Pushed at: about 3 years ago - Stars: 24 - Forks: 0

mayer79/flashlight

Machine learning explanations

Language: R - Size: 65.3 MB - Last synced at: 8 days ago - Pushed at: 3 months ago - Stars: 23 - Forks: 4

Graph-COM/LRI

[ICLR 2023] Learnable Randomness Injection (LRI) for interpretable Geometric Deep Learning.

Language: Python - Size: 937 KB - Last synced at: 3 months ago - Pushed at: almost 2 years ago - Stars: 23 - Forks: 3

schalkdaniel/compboost

C++ implementation and R API for componentwise boosting

Language: C++ - Size: 203 MB - Last synced at: 8 days ago - Pushed at: about 2 years ago - Stars: 23 - Forks: 3

Henrymachiyu/ProtoViT

This code implements ProtoViT, a novel approach that combines Vision Transformers with prototype-based learning to create interpretable image classification models. Our implementation provides both high accuracy and explainability through learned prototypes.

Language: Python - Size: 1020 KB - Last synced at: about 1 month ago - Pushed at: about 1 month ago - Stars: 22 - Forks: 7

JonathanCrabbe/Label-Free-XAI

This repository contains the implementation of Label-Free XAI, a new framework to adapt explanation methods to unsupervised models. For more details, please read our ICML 2022 paper: 'Label-Free Explainability for Unsupervised Models'.

Language: Python - Size: 8.19 MB - Last synced at: about 1 year ago - Pushed at: almost 3 years ago - Stars: 22 - Forks: 9

jphall663/hc_ml

Slides, videos and other potentially useful artifacts from various presentations on responsible machine learning.

Language: TeX - Size: 34.4 MB - Last synced at: 24 days ago - Pushed at: over 5 years ago - Stars: 22 - Forks: 8

dandls/counterfactuals

counterfactuals: An R package for Counterfactual Explanation Methods

Language: HTML - Size: 120 MB - Last synced at: 30 days ago - Pushed at: 8 months ago - Stars: 21 - Forks: 4

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: 11 days ago - Pushed at: over 2 years ago - Stars: 21 - Forks: 0

navdeep-G/interpretable-ml

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

Language: Jupyter Notebook - Size: 84.4 MB - Last synced at: 3 months ago - Pushed at: about 3 years ago - Stars: 21 - Forks: 8

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: about 1 month ago - Pushed at: 2 months ago - Stars: 19 - Forks: 5

benjaminpatrickevans/XAI

Genetic programming method for explaining complex black-box models

Language: Python - Size: 73.2 KB - Last synced at: about 13 hours ago - Pushed at: about 1 year ago - Stars: 19 - Forks: 3

12wang3/mllp

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

Language: Python - Size: 3.69 MB - Last synced at: about 1 year ago - Pushed at: over 1 year ago - Stars: 19 - Forks: 6

CoderPat/learning-scaffold

This is the official implementation for the paper "Learning to Scaffold: Optimizing Model Explanations for Teaching"

Language: Jupyter Notebook - Size: 3.75 MB - Last synced at: 2 months ago - Pushed at: about 3 years ago - Stars: 19 - Forks: 5

mertkosan/GCFExplainer

Global Counterfactual Explainer for Graph Neural Networks

Language: Python - Size: 16.3 MB - Last synced at: 3 months ago - Pushed at: over 2 years ago - Stars: 18 - Forks: 4

davideferrari92/multiobjective_symbolic_regression

This is a Python library that implements a Multi-objective Symbolic Regression algorithm. It can be used as a Machine Learning algorithm to create predictive models in the form of mathematical expressions.

Language: Python - Size: 1.1 MB - Last synced at: 3 months ago - Pushed at: 8 months ago - Stars: 17 - Forks: 5

utwente-dmb/xai-papers

Language: TypeScript - Size: 147 MB - Last synced at: about 1 month ago - Pushed at: 11 months ago - Stars: 17 - Forks: 9

khalooei/LSA

LSA : Layer Sustainability Analysis framework for the analysis of layer vulnerability in a given neural network. LSA can be a helpful toolkit to assess deep neural networks and to extend the adversarial training approaches towards improving the sustainability of model layers via layer monitoring and analysis.

Language: Python - Size: 33.8 MB - Last synced at: 27 days ago - Pushed at: over 3 years ago - Stars: 17 - Forks: 6