| Name | Version | Summary | date |
| antakia |
0.4.0 |
AI made Xplainable |
2024-04-18 15:17:48 |
| signxai |
1.1.7 |
SIGNed explanations: Unveiling relevant features by reducing bias |
2024-04-11 12:39:31 |
| dianna |
1.5.0 |
Deep Insight And Neural Network Analysis |
2024-04-10 15:49:22 |
| wpdm3333 |
0.1.0 |
Warsztat Praktyka Data Mining |
2024-03-12 23:16:21 |
| template-wpdm |
0.1.0 |
Warsztat Praktyka Data Mining |
2024-03-11 10:48:41 |
| affinitree |
0.21.1 |
Distillation of piece-wise linear neural networks into decision trees |
2024-03-08 17:05:11 |
| milosz7wpdm |
0.1.1 |
Warsztat Praktyka Data Mining |
2024-03-06 17:28:24 |
| tsproto |
0.1.3 |
Post-host prototype-based explanations with rules for time-series classifiers |
2024-03-05 07:24:21 |
| knac-toolkit |
1.0.2 |
Knowledge Augmented Clustering |
2024-02-29 10:37:49 |
| xai-explainer |
0.7.0 |
A package for explaining deep learning models |
2024-02-25 15:40:00 |
| lux-explainer |
1.0.0 |
Universal Local Rule-based Explainer |
2024-02-22 15:25:08 |
| slisemap-interactive |
0.6.0 |
Interactive plots for Slisemap using Dash |
2024-02-20 13:41:33 |
| easy-explain |
0.5.0 |
A library that helps to explain AI models in a really quick & easy way |
2024-02-12 14:27:32 |
| trustyai |
0.5.0 |
Python bindings to the TrustyAI explainability library. |
2024-02-02 13:31:44 |
| pyxai |
1.0.12 |
Explaining Machine Learning Classifiers in Python |
2024-02-02 08:49:41 |
| multixai |
1.0.0 |
|
2024-01-21 05:59:27 |
| rules-extraction |
0.1.0 |
Rules extraction for eXplainable AI |
2024-01-17 08:52:49 |
| hoocs |
0.0.1 |
Occlusion-based explainers for higher-order attributions. |
2024-01-16 13:15:36 |
| aequitas-core |
1.2.1 |
Aequitas core library |
2023-12-14 15:48:04 |
| quantus |
0.5.3 |
A metrics toolkit to evaluate neural network explanations. |
2023-12-05 11:42:49 |