ferrolearn-linear

Linear models for the ferrolearn machine learning framework. Validated against scikit-learn 1.8.0 head-to-head — see the workspace BENCHMARKS.md for the full report.

Algorithms

Regression

Model	Description
`LinearRegression`	Ordinary Least Squares via Cholesky / QR fallback
`Ridge`	L2-regularized regression via Cholesky decomposition
`RidgeCV`	Ridge with built-in alpha cross-validation
`Lasso`	L1-regularized regression via coordinate descent
`LassoCV`	Lasso with alpha CV
`LassoLars`	Lasso via Least-Angle Regression
`Lars`	Least-Angle Regression
`OrthogonalMatchingPursuit`	Sparse coding via OMP
`ElasticNet` / `ElasticNetCV`	Combined L1/L2 regularization
`BayesianRidge`	Bayesian ridge with automatic regularization tuning
`ARDRegression`	Automatic Relevance Determination
`HuberRegressor`	Robust regression via IRLS with Huber loss
`QuantileRegressor`	L1-regularized quantile regression (sklearn-equivalent α scale)
`RANSACRegressor`	Robust regression with outlier rejection
`SGDRegressor`	Stochastic gradient descent regressor
`LinearSVR` / `NuSVR`	Linear / Nu Support Vector Regression
`IsotonicRegression`	Non-decreasing 1D regression

Classification

Model	Description
`LogisticRegression`	Binary and multiclass classification via L-BFGS
`LogisticRegressionCV`	Logistic regression with C cross-validation
`RidgeClassifier`	Ridge regression cast as a classifier
`LinearSVC`	Linear Support Vector Classifier (coordinate-Newton primal solver)
`NuSVC` / `SVC`	Kernel SVMs
`OneClassSVM`	Novelty / outlier detection
`LDA`	Linear Discriminant Analysis
`QDA`	Quadratic Discriminant Analysis
`SGDClassifier`	Stochastic gradient descent classifier

Example

use ferrolearn_linear::{Ridge, FittedRidge};
use ferrolearn_core::{Fit, Predict};
use ndarray::array;

let x = array![[1.0_f64, 2.0], [3.0, 4.0], [5.0, 6.0]];
let y = array![1.0, 2.0, 3.0];

let model = Ridge::<f64>::new().with_alpha(1.0);
let fitted = model.fit(&x, &y).unwrap();
let predictions = fitted.predict(&x).unwrap();

All models follow the compile-time safety pattern: unfitted structs implement Fit, fitted structs implement Predict. Calling predict() on an unfitted model is a compile error.

sklearn parity highlights (0.3.0)

LinearSVC was rewritten with coordinate-Newton steps replacing fixed-step gradient descent — closed a -21pp accuracy gap at medium scale.
QuantileRegressor's alpha was rescaled by n_samples so it's numerically equivalent to scikit-learn's alpha.
All regressor and classifier defaults now match scikit-learn ≥ 1.4.

Float generics

All models are generic over F: Float + Send + Sync + 'static, supporting both f32 and f64.

License

Licensed under either of Apache License, Version 2.0 or MIT License at your option.

ferrolearn-linear 0.3.0