15 results found Sort:

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
Created 2018-03-14
169 commits to master branch, last one about a year ago
33
107
mit
4
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 Classif...
Created 2021-09-30
10 commits to main branch, last one 8 months ago
16
103
unknown
11
Model Agnostics breakDown plots
Created 2017-11-18
102 commits to master branch, last one 8 months ago
A Julia package for interpretable machine learning with stochastic Shapley values
Created 2020-01-23
118 commits to master branch, last one 2 years ago
Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)
Created 2018-10-17
246 commits to master branch, last one about a year ago
Compute SHAP values for your tree-based models using the TreeSHAP algorithm
Created 2020-07-30
232 commits to master branch, last one 10 months ago
12
78
cc-by-4.0
3
[NeurIPS'24 Spotlight] A comprehensive benchmark & codebase for Image manipulation detection/localization.
Created 2024-06-10
94 commits to main branch, last one 5 days ago
Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.
Created 2019-06-27
102 commits to master branch, last one 2 years ago
11
72
apache-2.0
7
An interactive framework to visualize and analyze your AutoML process in real-time.
Created 2021-05-04
949 commits to main branch, last one 3 months ago
9
68
unknown
4
Unofficial implementation of MVSS-Net (ICCV 2021) with Pytorch including training code.
Created 2023-01-11
30 commits to main branch, last one about a year ago
1
54
gpl-3.0
2
Effector - a Python package for global and regional effect methods
Created 2022-03-31
345 commits to main branch, last one 6 months ago