Crate anofox_ml_regression 

Classical regression models wrapping the anofox-regression crate.

Provides OLS, Ridge, Lasso, Elastic Net, WLS, Quantile, Isotonic, and GLM (Poisson, Binomial) regressors that implement the anofox-ml Fit / Predict type-state pattern.

Cross-validated variants (RidgeCrossValidated, LassoCrossValidated, ElasticNetCrossValidated) automatically select the best regularization parameters using k-fold cross-validation.

All models operate on f64 (not generic over Float) because the underlying anofox-regression crate only supports f64.

Re-exports

pub use bayesian_ridge::ARDRegression;

pub use bayesian_ridge::BayesianRidge;

pub use bayesian_ridge::FittedARDRegression;

pub use bayesian_ridge::FittedBayesianRidge;

pub use elastic_net::ElasticNetRegressor;

pub use elastic_net::FittedElasticNetRegressor;

pub use elastic_net_cv::ElasticNetCrossValidated;

pub use elastic_net_cv::FittedElasticNetCrossValidated;

pub use glm::BinomialRegressor;

pub use glm::FittedBinomialRegressor;

pub use glm::FittedPoissonRegressor;

pub use glm::PoissonRegressor;

pub use huber::FittedHuberRegressor;

pub use huber::HuberRegressor;

pub use isotonic::FittedIsotonicRegressor;

pub use isotonic::IsotonicRegressor;

pub use kernel_ridge::FittedKernelRidge;

pub use kernel_ridge::KernelRidge;

pub use lars::FittedLars;

pub use lars::FittedLassoLarsIC;

pub use lars::IcCriterion;

pub use lars::Lars;

pub use lars::LassoLarsIC;

pub use lasso::FittedLassoRegressor;

pub use lasso::LassoRegressor;

pub use lasso_cv::FittedLassoCrossValidated;

pub use lasso_cv::LassoCrossValidated;

pub use logistic::FittedLogisticRegressor;

pub use logistic::LogisticRegressor;

pub use ols::FittedOlsRegressor;

pub use ols::OlsRegressor;

pub use omp::FittedOrthogonalMatchingPursuit;

pub use omp::OrthogonalMatchingPursuit;

pub use quantile::FittedQuantileRegressor;

pub use quantile::QuantileRegressor;

pub use ridge::FittedRidgeRegressor;

pub use ridge::FittedWeightedRidgeRegressor;

pub use ridge::RidgeRegressor;

pub use ridge_cv::FittedRidgeCrossValidated;

pub use ridge_cv::RidgeCrossValidated;

pub use robust::FittedRansacRegressor;

pub use robust::FittedTheilSenRegressor;

pub use robust::RansacRegressor;

pub use robust::TheilSenRegressor;

pub use transformed_target::FittedTransformedTargetRegressor;

pub use transformed_target::TransformedTargetRegressor;

pub use tweedie::gamma_regressor;

pub use tweedie::FittedGammaRegressor;

pub use tweedie::FittedTweedieRegressor;

pub use tweedie::GammaRegressor;

pub use tweedie::TweedieRegressor;

pub use wls::FittedWlsRegressor;

pub use wls::WlsRegressor;

Modules

bayesian_ridge

Bayesian Ridge Regression and Automatic Relevance Determination (ARD).

convert

Conversion helpers between ndarray and faer types.

elastic_net

Elastic Net regression wrapper (combined L1 and L2 regularization).

elastic_net_cv

Elastic Net regression with built-in cross-validation for hyperparameter selection.

glm

GLM (Generalized Linear Model) wrappers for Poisson and Binomial regression.

huber

Huber robust regression wrapper.

isotonic

Isotonic (monotonic) regression wrapper.

kernel_ridge

Kernel ridge regression.

lars

Least Angle Regression (LARS) and LASSO LARS variants.

lasso

Lasso (L1-regularized) regression wrapper.

lasso_cv

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

logistic

Logistic regression classifier wrapper.

ols

Ordinary Least Squares regression wrapper.

omp

Orthogonal Matching Pursuit.

quantile

Quantile regression wrapper.

ridge

Ridge (L2-regularized) regression wrapper.

ridge_cv

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

robust

Robust regression: TheilSen and RANSAC.

transformed_target

Meta-estimator that transforms y before fitting and inverts on prediction.

tweedie

Tweedie / Gamma GLM regressors.

wls

Weighted Least Squares regression wrapper.