| Name | Version | Summary | date |
| nnetsauce |
0.27.0 |
Quasi-randomized (neural) networks |
2024-11-13 16:30:10 |
| learningmachine |
2.7.0 |
Machine Learning with uncertainty quantification and interpretability |
2024-11-08 11:18:41 |
| glmnetforpython |
0.3.0 |
Python version of glmnet, adapted from Stanford University (scikit-learn style) |
2024-11-07 15:39:49 |
| forecastingapi |
0.4.0 |
Tools for interacting with Techtonique API |
2024-11-04 08:03:12 |
| ahead |
0.13.0 |
Time series forecasting with Machine Learning and uncertainty quantification |
2024-09-16 20:47:06 |
| unifiedbooster |
0.7.0 |
Unified interface for Gradient Boosted Decision Trees |
2024-09-02 09:16:01 |
| the-teller |
0.9.1 |
teller, explainable machine learning |
2024-08-07 06:33:31 |
| rtopy |
0.1.2 |
Call R functions from Python |
2024-03-05 10:08:02 |
| BCN |
0.7.1 |
Boosted Configuration Networks |
2024-02-05 23:26:38 |
| esgtoolkit |
1.1.0 |
Diffusion models for finance, insurance, economics, physics |
2023-12-18 21:49:52 |
T. Moudiki