sklearn.linear_model.rst.txt

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sklearn.linear_model

.. automodule:: sklearn.linear_model

User guide. See the :ref:`linear_model` 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

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  LogisticRegression
  LogisticRegressionCV
  PassiveAggressiveClassifier
  Perceptron
  RidgeClassifier
  RidgeClassifierCV
  SGDClassifier
  SGDOneClassSVM

Classical linear regressors

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  LinearRegression
  Ridge
  RidgeCV
  SGDRegressor

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 :class:`~linear_model.SGDRegressor` or :class:`~sklearn.linear_model.SGDClassifier` with an appropriate penalty.

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  ElasticNet
  ElasticNetCV
  Lars
  LarsCV
  Lasso
  LassoCV
  LassoLars
  LassoLarsCV
  LassoLarsIC
  OrthogonalMatchingPursuit
  OrthogonalMatchingPursuitCV

Bayesian regressors

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  ARDRegression
  BayesianRidge

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).

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  MultiTaskElasticNet
  MultiTaskElasticNetCV
  MultiTaskLasso
  MultiTaskLassoCV

Outlier-robust regressors

Any estimator using the Huber loss would also be robust to outliers, e.g., :class:`~linear_model.SGDRegressor` with loss='huber'.

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  HuberRegressor
  QuantileRegressor
  RANSACRegressor
  TheilSenRegressor

Generalized linear models (GLM) for regression

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

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  GammaRegressor
  PoissonRegressor
  TweedieRegressor

Miscellaneous

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  PassiveAggressiveRegressor
  enet_path
  lars_path
  lars_path_gram
  lasso_path
  orthogonal_mp
  orthogonal_mp_gram
  ridge_regression