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'.