| Name | Version | Summary | date |
| pyartemis |
0.1.5 |
A Python package with explanation methods for extraction of feature interactions from predictive models |
2023-11-18 00:25:13 |
| corr-shap |
0.0.2 |
This package is an extension of the KernelExplainer of shap package that explains the output of any machine learning model, taking into account dependencies between features. |
2023-11-14 11:04:28 |
| xai_explainer |
0.2.3 |
A package for explaining deep learning models |
2023-11-04 11:48:10 |
| innvestigate |
2.1.2 |
A toolbox to innvestigate neural networks' predictions. |
2023-10-12 14:56:02 |
| aequitas-lib |
1.0.0 |
Core library of the Aequitas project |
2023-10-11 13:46:23 |
| intel-xai |
0.6.0 |
Intel® Explainable AI Tools |
2023-10-06 23:13:28 |
| dimlpfidex |
0.0.0 |
Discretized Interpretable Multi Layer Perceptron (DIMLP) and related algorithms |
2023-09-26 08:53:02 |
| xi-method |
0.1.7 |
Post hoc explanations for ML models through measures of statistical dependence |
2023-09-22 15:34:19 |
| metaquantus |
0.0.5 |
MetaQuantus is a XAI performance tool for identifying reliable metrics. |
2023-09-13 09:32:57 |
| xai-kit |
0.0.7 |
Explainable Artificial Intelligence (XAI) Library |
2023-09-10 23:25:22 |
| xaiz |
0.0.3 |
Explainable Artificial Intelligence (XAI) Library |
2023-09-09 17:29:21 |
| XAISuite |
2.9.2 |
XAISuite: Training and Explanation Generation Utilities for Machine Learning Models |
2023-09-05 01:21:31 |
| pyxai-experimental |
1.0.post1 |
Explaining Machine Learning Classifiers in Python |
2023-09-04 10:58:11 |
| revelionn |
1.0.2 |
Retrospective Extraction of Visual and Logical Insights for Ontology-based interpretation of Neural Networks |
2023-09-04 01:27:09 |
| itershap |
0.1.1 |
Iterative feature selection method using SHAP values |
2023-08-16 08:56:45 |
| manic-xai |
1.0.81 |
Genetic Algorithm for Generating Metacounterfactual Explanations |
2023-08-15 00:14:35 |
| ClarityAI |
1.0.0 |
ClarityAI is a Python package designed to empower machine learning practitioners with a wide range of interpretability methods to enhance the transparency and explainability of their ML models. |
2023-08-12 20:26:16 |
| contrastive-xai |
0.1.1 |
Contrastive Explainable AI Algorithms |
2023-07-31 02:54:27 |
| prolog-primitives |
1.2.2 |
description here |
2023-07-21 18:08:10 |
| omnixai |
1.3.1 |
OmniXAI: An Explainable AI Toolbox |
2023-07-16 04:58:16 |