| Name | Version | Summary | date |
| tefs |
1.0.0 |
Causal feature selection for time series data using transfer entropy |
2024-12-28 21:25:35 |
| pSevenCore |
2024.12.27 |
pSeven Core is an integrated toolkit for design space exploration, optimization, and predictive modeling. |
2024-12-27 12:31:11 |
| xnb |
0.3.0 |
Explainable Naive Bayes (XNB) classifier. Using KDE for feature selection and Naive Bayes for prediction. |
2024-11-20 17:29:39 |
| adaptivepca |
1.1.3 |
An advanced PCA implementation with adaptive feature scaling and preprocessing |
2024-10-31 03:51:45 |
| featransform |
0.9.21 |
Featransform is an automated feature engineering framework for Supervised Machine Learning |
2024-10-24 18:29:48 |
| csd |
1.0.0 |
(Constrained) subgroup discovery |
2024-10-04 11:18:13 |
| hi-lasso2 |
0.0.1 |
High-Demensional LASSO2 |
2024-09-30 23:25:23 |
| NiaAML-GUI |
0.3.2 |
GUI for NiaAML Python package |
2024-08-21 11:09:51 |
| xai-feature-selection |
0.6 |
Feature selection using XAI |
2024-07-13 20:08:15 |
| autofeat |
2.1.3 |
Automatic Feature Engineering and Selection Linear Prediction Model |
2024-07-04 18:24:37 |
| mafese |
0.2.0 |
Feature Selection using Metaheuristics Made Easy: Open Source MAFESE Library in Python |
2024-06-12 15:47:19 |
| NiaAML |
2.1.0 |
Python automated machine learning framework |
2024-06-12 12:19:11 |
| triglav |
1.0.7 |
Triglav: Iterative Refinement and Selection of Stable Features Using Shapley Values |
2024-05-14 14:48:22 |
| featuristic |
1.1.0 |
Genetic algorithms for automated feature engineering and feature selection |
2024-04-10 14:40:08 |
| feature-engineering |
2.1.4 |
Unleash the Power of Your Data with Feature Engineering: The Ultimate Python Library for Machine Learning Preprocessing and Enhancement |
2024-04-09 02:00:21 |