A scikit-learn equivalent for Rust — re-export crate
Core traits, error types, and pipeline for the ferrolearn ML framework
Serialization and I/O utilities for the ferrolearn ML framework
Numerical foundations for the ferrolearn ML framework: sparse eigensolvers, graph algorithms, distributions, optimization, interpolation, and quadrature
Python bindings for ferrolearn via PyO3
Nearest neighbor models for the ferrolearn ML framework
Bayesian methods for the ferrolearn ML framework — naive Bayes classifiers and conjugate priors
Clustering algorithms for the ferrolearn ML framework
Dimensionality reduction and decomposition for the ferrolearn ML framework
Kernel methods for the ferrolearn ML framework: Nadaraya-Watson and local polynomial regression, Gaussian processes (regression and Laplace-approximation classification), kernel ridge, Nyström approximation, and RBF random Fourier features
Linear models for the ferrolearn ML framework
Classification and regression metrics for the ferrolearn ML framework