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46 search results found

Awesome Machine Learning Interpretability ⭐ 3,241

A curated list of awesome responsible machine learning resources.

Dalex ⭐ 1,242

moDel Agnostic Language for Exploration and eXplanation

Awesome Interpretable Machine Learning ⭐ 856

[HELP REQUESTED] Generalized Additive Models in Python

Interpretable_machine_learning_with_python ⭐ 629

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

Mli Resources ⭐ 405

H2O.ai Machine Learning Interpretability Resources

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

Etsformer ⭐ 131

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

Conceptbottleneck ⭐ 99

Concept Bottleneck Models, ICML 2020

Tsinterpret ⭐ 83

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

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"

Talktomodel ⭐ 68

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

Machine Learning ⭐ 68

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

Information Bottlenecks for Attribution

Layerwise Relevance Propagation ⭐ 51

Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers

Prototree ⭐ 46

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

Model Agnostic Counterfactual Explanations

Diabetes_use_case ⭐ 19

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

a tool for comparing the predictions of any text classifiers

Interpretable Ml ⭐ 17

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

Survlimepy ⭐ 16

Local interpretability for survival models

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

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

Lucidmode ⭐ 14

A Lucid Framework for Transparent and Interpretable Machine Learning Models.

MERLIN is a global, model-agnostic, contrastive explainer for any tabular or text classifier. It provides contrastive explanations of how the behaviour of two machine learning models differs.

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

Article Information 2019 ⭐ 10

Article for Special Edition of Information: Machine Learning with Python

Label Free Xai ⭐ 10

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'.

ICLR'22 Programmatic Reinforcement Learning

Autopeptideml ⭐ 9

AutoML system for building trustworthy peptide bioactivity predictors

Ml Flask Api ⭐ 9

A simple template of a Python API (web-service) for real-time Machine Learning predictions, using scikitlearn-like models, Flask and Docker.

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

Jsm_2018_paper ⭐ 8

Paper for 2018 Joint Statistical Meetings: https://ww2.amstat.org/meetings/jsm/2018/onlinepro

Gcfexplainer ⭐ 6

Global Counterfactual Explainer for Graph Neural Networks

Deep learning for personalized interpretability for compositional health data

Multiobjective_symbolic_regression ⭐ 6

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.

Shapley_valuation ⭐ 6

PyTorch reimplementation of computing Shapley values via Truncated Monte Carlo sampling from "What is your data worth? Equitable Valuation of Data" by Amirata Ghorbani and James Zou [ICML 2019]

Visualime ⭐ 5

Implementation of LIME focused on producing user-centric local explanations for image classifiers.

Dynamic Shap Plots ⭐ 5

Enabling interactive plotting of the visualizations from the SHAP project.

Robust regression algorithm that can be used for explaining black box models (Python implementation)

Xi Method ⭐ 5

RuleCOSI is a machine learning package that combine and simplifies tree ensembles and generates a single rule-based classifier that is smaller and simpler.

Functional_attribution ⭐ 5

Code of our accepted ICML 2021 paper "Towards Rigorous Interpretations: a Formalisation of Feature Attribution" (D. Afchar, R. Hennequin, V. Guigue)

Intro To Transparent Ml Course ⭐ 5

An Introduction to Transparent Machine Learning

Official Implementation of "E pluribus unum interpretable convolutional neural networks"

XAISuite: Train machine learning models, generate explanations, and compare different explanation systems with just a simple line of code.

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