| Name | Version | Summary | date |
|---|---|---|---|
| mlrl-seco | 0.12.2 | A scikit-learn implementation of a Separate-and-Conquer (SeCo) multi-label rule learning algorithm | 2025-07-31 22:02:58 |
| mlrl-common | 0.12.2 | Provides common modules to be used by different types of multi-label rule learning algorithms | 2025-07-31 22:02:37 |
| mlrl-boomer | 0.12.2 | A scikit-learn implementation of BOOMER - an algorithm for learning gradient boosted multi-label output rules | 2025-07-31 22:02:15 |
| 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 |
|---|---|---|---|
| 114 | 2204 | 10472 | 305375 |