| Name | Version | Summary | date |
|---|---|---|---|
| mlrl-seco | 0.12.3 | A scikit-learn implementation of a Separate-and-Conquer (SeCo) multi-label rule learning algorithm | 2025-08-02 16:56:23 |
| mlrl-common | 0.12.3 | Provides common modules to be used by different types of multi-label rule learning algorithms | 2025-08-02 16:56:04 |
| mlrl-boomer | 0.12.3 | A scikit-learn implementation of BOOMER - an algorithm for learning gradient boosted multi-label output rules | 2025-08-02 16:55:44 |
| PyEDCR | 1.1.3 | PyEDCR is a metacognitive neuro-symbolic method for learning error detection and correction rules in deployed ML models using combinatorial sub-modular set optimization | 2025-02-19 06:34:54 |
| hour | day | week | total |
|---|---|---|---|
| 54 | 1325 | 10530 | 305989 |