ml
Supervised Learning with Mandatory Splits and Seeds
Implements the split-fit-evaluate-assess workflow from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0-387-84857-0) "The Elements of Statistical Learning", Chapter 7. Provides three-way data splitting with automatic stratification, mandatory seeds for reproducibility, automatic data type handling, and 10 algorithms out of the box. Uses 'Rust' backend for cross-language deterministic splitting. Designed for tabular supervised learning with minimal ceremony. Polyglot parity with the 'Python' 'mlw' package on 'PyPI'.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.2 |
rolling linux/jammy R-4.5 | ml_0.1.2.tar.gz |
576.9 KiB |
0.1.2 |
rolling linux/noble R-4.5 | ml_0.1.2.tar.gz |
577.2 KiB |
0.1.2 |
rolling source/ R- | ml_0.1.2.tar.gz |
156.2 KiB |
0.1.2 |
latest linux/jammy R-4.5 | ml_0.1.2.tar.gz |
576.9 KiB |
0.1.2 |
latest linux/noble R-4.5 | ml_0.1.2.tar.gz |
577.2 KiB |
0.1.2 |
latest source/ R- | ml_0.1.2.tar.gz |
156.2 KiB |
0.1.2 |
2026-04-26 source/ R- | ml_0.1.2.tar.gz |
156.2 KiB |
0.1.2 |
2026-04-23 source/ R- | ml_0.1.2.tar.gz |
156.2 KiB |
0.1.2 |
2026-04-09 windows/windows R-4.5 | ml_0.1.2.zip |
584.8 KiB |