sklearn.linear_model

A variety of linear models.

User guide. See the Linear Models section for further details.

The following subsections are only rough guidelines: the same estimator can fall into multiple categories, depending on its parameters.

Linear classifiers

LogisticRegression	Logistic Regression (aka logit, MaxEnt) classifier.
LogisticRegressionCV	Logistic Regression CV (aka logit, MaxEnt) classifier.
PassiveAggressiveClassifier	Passive Aggressive Classifier.
Perceptron	Linear perceptron classifier.
RidgeClassifier	Classifier using Ridge regression.
RidgeClassifierCV	Ridge classifier with built-in cross-validation.
SgDClassifier	Linear classifiers (SVM, logistic regression, etc.) with SgD training.
SgDOneClassSVM	Solves linear One-Class SVM using Stochastic gradient Descent.

Classical linear regressors

LinearRegression	Ordinary least squares Linear Regression.
Ridge	Linear least squares with l2 regularization.
RidgeCV	Ridge regression with built-in cross-validation.
SgDRegressor	Linear model fitted by minimizing a regularized empirical loss with SgD.

Regressors with variable selection

The following estimators have built-in variable selection fitting procedures, but any estimator using a L1 or elastic-net penalty also performs variable selection: typically SgDRegressor or SgDClassifier with an appropriate penalty.

ElasticNet	Linear regression with combined L1 and L2 priors as regularizer.
ElasticNetCV	Elastic Net model with iterative fitting along a regularization path.
Lars	Least Angle Regression model a.k.a.
LarsCV	Cross-validated Least Angle Regression model.
Lasso	Linear Model trained with L1 prior as regularizer (aka the Lasso).
LassoCV	Lasso linear model with iterative fitting along a regularization path.
LassoLars	Lasso model fit with Least Angle Regression a.k.a.
LassoLarsCV	Cross-validated Lasso, using the LARS algorithm.
LassoLarsIC	Lasso model fit with Lars using BIC or AIC for model selection.
OrthogonalMatchingPursuit	Orthogonal Matching Pursuit model (OMP).
OrthogonalMatchingPursuitCV	Cross-validated Orthogonal Matching Pursuit model (OMP).

Bayesian regressors

ARDRegression	Bayesian ARD regression.
BayesianRidge	Bayesian ridge regression.

Multi-task linear regressors with variable selection

These estimators fit multiple regression problems (or tasks) jointly, while inducing sparse coefficients. While the inferred coefficients may differ between the tasks, they are constrained to agree on the features that are selected (non-zero coefficients).

MultiTaskElasticNet	Multi-task ElasticNet model trained with L1/L2 mixed-norm as regularizer.
MultiTaskElasticNetCV	Multi-task L1/L2 ElasticNet with built-in cross-validation.
MultiTaskLasso	Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.
MultiTaskLassoCV	Multi-task Lasso model trained with L1/L2 mixed-norm as regularizer.

Outlier-robust regressors

Any estimator using the Huber loss would also be robust to outliers, e.g., SgDRegressor with loss='huber'.

HuberRegressor	L2-regularized linear regression model that is robust to outliers.
QuantileRegressor	Linear regression model that predicts conditional quantiles.
RANSACRegressor	RANSAC (RANdom SAmple Consensus) algorithm.
TheilSenRegressor	Theil-Sen Estimator: robust multivariate regression model.

generalized linear models (gLM) for regression

These models allow for response variables to have error distributions other than a normal distribution.

gammaRegressor	generalized Linear Model with a gamma distribution.
PoissonRegressor	generalized Linear Model with a Poisson distribution.
TweedieRegressor	generalized Linear Model with a Tweedie distribution.

Miscellaneous

PassiveAggressiveRegressor	Passive Aggressive Regressor.
enet_path	Compute elastic net path with coordinate descent.
lars_path	Compute Least Angle Regression or Lasso path using the LARS algorithm.
lars_path_gram	The lars_path in the sufficient stats mode.
lasso_path	Compute Lasso path with coordinate descent.
orthogonal_mp	Orthogonal Matching Pursuit (OMP).
orthogonal_mp_gram	gram Orthogonal Matching Pursuit (OMP).
ridge_regression	Solve the ridge equation by the method of normal equations.