| Name | Version | Summary | date |
| mlrl-boomer |
0.11.1 |
A scikit-learn implementation of BOOMER - an algorithm for learning gradient boosted multi-label classification rules |
2024-09-24 21:25:46 |
| mlrl-seco |
0.11.1 |
A scikit-learn implementation of a separate-and-conquer multi-label rule learning algorithm |
2024-09-24 21:24:45 |
| mlrl-common |
0.11.1 |
Provides common modules to be used by different types of multi-label rule learning algorithms |
2024-09-24 21:23:57 |
| mlrl-testbed |
0.11.1 |
Provides utilities for the training and evaluation of machine learning algorithms |
2024-09-24 21:20:01 |
| example-wise-f1-maximizer |
0.1.5 |
A scikit-learn meta-estimator for multi-label classification that aims to maximize the example-wise F1 measure |
2023-06-16 11:15:57 |
Michael Rapp