Crate ferrolearn_linear 

ferrolearn-linear

Linear models for the ferrolearn machine learning framework.

This crate provides implementations of the most common linear models for both regression and classification tasks:

• LinearRegression — Ordinary Least Squares via QR decomposition
• Ridge — L2-regularized regression via Cholesky decomposition
• Lasso — L1-regularized regression via coordinate descent
• ElasticNet — Combined L1/L2 regularization via coordinate descent
• BayesianRidge — Bayesian Ridge with automatic regularization tuning
• HuberRegressor — Robust regression via IRLS with Huber loss
• LogisticRegression — Binary and multiclass classification via L-BFGS

All models implement the ferrolearn_core::Fit and ferrolearn_core::Predict traits, and produce fitted types that implement ferrolearn_core::introspection::HasCoefficients.

Design

Each model follows the compile-time safety pattern:

• The unfitted struct (e.g., LinearRegression<F>) holds hyperparameters and implements Fit.
• Calling fit() produces a new fitted type (e.g., FittedLinearRegression<F>) that implements Predict.
• Calling predict() on an unfitted model is a compile-time error.

Pipeline Integration

All models implement PipelineEstimator for f64, allowing them to be used as the final step in a Pipeline.

Float Generics

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

Re-exports

pub use bayesian_ridge::BayesianRidge;

pub use bayesian_ridge::FittedBayesianRidge;

pub use elastic_net::ElasticNet;

pub use elastic_net::FittedElasticNet;

pub use huber_regressor::FittedHuberRegressor;

pub use huber_regressor::HuberRegressor;

pub use lasso::FittedLasso;

pub use lasso::Lasso;

pub use lda::FittedLDA;

pub use lda::LDA;

pub use linear_regression::FittedLinearRegression;

pub use linear_regression::LinearRegression;

pub use logistic_regression::FittedLogisticRegression;

pub use logistic_regression::LogisticRegression;

pub use ridge::FittedRidge;

pub use ridge::Ridge;

pub use sgd::FittedSGDClassifier;

pub use sgd::FittedSGDRegressor;

pub use sgd::SGDClassifier;

pub use sgd::SGDRegressor;

Modules

bayesian_ridge

Bayesian Ridge Regression.

elastic_net

ElasticNet regression (combined L1 and L2 regularization).

huber_regressor

Huber Regressor — robust regression via IRLS.

lasso

Lasso regression (L1-regularized linear regression).

lda

Linear Discriminant Analysis (LDA).

linear_regression

Ordinary Least Squares linear regression.

logistic_regression

Logistic regression classifier.

ridge

Ridge regression (L2-regularized linear regression).

sgd

Stochastic Gradient Descent (SGD) linear models.