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
• RidgeCV — Ridge with built-in cross-validated alpha selection
• Lasso — L1-regularized regression via coordinate descent
• LassoCV — Lasso with built-in cross-validated alpha selection
• ElasticNet — Combined L1/L2 regularization via coordinate descent
• ElasticNetCV — ElasticNet with cross-validated (alpha, l1_ratio) selection
• 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 ard::ARDRegression;

pub use ard::FittedARDRegression;

pub use bayesian_ridge::BayesianRidge;

pub use bayesian_ridge::FittedBayesianRidge;

pub use elastic_net::ElasticNet;

pub use elastic_net::FittedElasticNet;

pub use elastic_net_cv::ElasticNetCV;

pub use elastic_net_cv::FittedElasticNetCV;

pub use glm::FittedGLMRegressor;

pub use glm::GLMFamily;

pub use glm::GLMRegressor;

pub use glm::GammaRegressor;

pub use glm::PoissonRegressor;

pub use glm::TweedieRegressor;

pub use huber_regressor::FittedHuberRegressor;

pub use huber_regressor::HuberRegressor;

pub use isotonic::FittedIsotonicRegression;

pub use isotonic::IsotonicRegression;

pub use lars::FittedLars;

pub use lars::FittedLassoLars;

pub use lars::Lars;

pub use lars::LassoLars;

pub use lasso::FittedLasso;

pub use lasso::Lasso;

pub use lasso_cv::FittedLassoCV;

pub use lasso_cv::LassoCV;

pub use lda::FittedLDA;

pub use lda::LDA;

pub use linear_regression::FittedLinearRegression;

pub use linear_regression::LinearRegression;

pub use linear_svc::FittedLinearSVC;

pub use linear_svc::LinearSVC;

pub use linear_svc::LinearSVCLoss;

pub use linear_svr::FittedLinearSVR;

pub use linear_svr::LinearSVR;

pub use linear_svr::LinearSVRLoss;

pub use logistic_regression::FittedLogisticRegression;

pub use logistic_regression::LogisticRegression;

pub use logistic_regression_cv::FittedLogisticRegressionCV;

pub use logistic_regression_cv::LogisticRegressionCV;

pub use nu_svm::FittedNuSVC;

pub use nu_svm::FittedNuSVR;

pub use nu_svm::NuSVC;

pub use nu_svm::NuSVR;

pub use omp::FittedOMP;

pub use omp::OrthogonalMatchingPursuit;

pub use one_class_svm::FittedOneClassSVM;

pub use one_class_svm::OneClassSVM;

pub use qda::FittedQDA;

pub use qda::QDA;

pub use quantile_regressor::FittedQuantileRegressor;

pub use quantile_regressor::QuantileRegressor;

pub use ransac::FittedRANSACRegressor;

pub use ransac::RANSACRegressor;

pub use ridge::FittedRidge;

pub use ridge::Ridge;

pub use ridge_classifier::FittedRidgeClassifier;

pub use ridge_classifier::RidgeClassifier;

pub use ridge_cv::FittedRidgeCV;

pub use ridge_cv::RidgeCV;

pub use sgd::FittedSGDClassifier;

pub use sgd::FittedSGDRegressor;

pub use sgd::SGDClassifier;

pub use sgd::SGDRegressor;

pub use svm::FittedSVC;

pub use svm::FittedSVR;

pub use svm::Kernel;

pub use svm::LinearKernel;

pub use svm::PolynomialKernel;

pub use svm::RbfKernel;

pub use svm::SVC;

pub use svm::SVR;

pub use svm::SigmoidKernel;

Modules

ard

Automatic Relevance Determination (ARD) Regression.

bayesian_ridge

Bayesian Ridge Regression.

elastic_net

ElasticNet regression (combined L1 and L2 regularization).

elastic_net_cv

ElasticNet regression with built-in cross-validation for alpha and l1_ratio selection.

glm

Generalized Linear Models (GLM).

huber_regressor

Huber Regressor — robust regression via IRLS.

isotonic

Isotonic (monotonic) regression.

lars

Least Angle Regression (LARS) and Lasso-LARS.

lasso

Lasso regression (L1-regularized linear regression).

lasso_cv

Lasso regression with built-in cross-validation for alpha selection.

lda

Linear Discriminant Analysis (LDA).

linear_regression

Ordinary Least Squares linear regression.

linear_svc

Linear Support Vector Classifier.

linear_svr

Linear Support Vector Regressor.

logistic_regression

Logistic regression classifier.

logistic_regression_cv

Logistic Regression with built-in cross-validated C selection.

nu_svm

Nu-parameterized Support Vector Machines.

omp

Orthogonal Matching Pursuit (OMP).

one_class_svm

One-Class SVM for novelty detection.

qda

Quadratic Discriminant Analysis (QDA).

quantile_regressor

Quantile Regression via IRLS on the pinball loss.

ransac

RANSAC (RANdom SAmple Consensus) robust regression.

ridge

Ridge regression (L2-regularized linear regression).

ridge_classifier

Ridge Classifier.

ridge_cv

Ridge regression with built-in cross-validation for alpha selection.

sgd

Stochastic Gradient Descent (SGD) linear models.

svm

Support Vector Machine with kernel trick.

Traits

ClassifierScore

Mean-accuracy score(x, y) exposed on every fitted classifier in this crate via a blanket impl over Predict<Array2<F>, Output=Array1<usize>>.

RegressorScore

R² score(x, y) exposed on every fitted regressor in this crate via a blanket impl over Predict<Array2<F>, Output=Array1<F>>.