29 lines
1016 B
Python
29 lines
1016 B
Python
"""
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:mod:`scikits.learn.linear_model` is a module to fit genelarized linear
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models. It includes Ridge regression, Bayesian Regression, Lasso and
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Elastic Net estimators computed with Least Angle Regression and
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coordinate descent.
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It also implements Stochastic Gradient Descent related algorithms.
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"""
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# See http://scikit-learn.sourceforge.net/modules/sgd.html and
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# http://scikit-learn.sourceforge.net/modules/linear_model.html for
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# complete documentation.
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from .base import LinearRegression, Log, ModifiedHuber, Hinge, \
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SquaredLoss, Huber
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from .bayes import BayesianRidge, ARDRegression
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from .least_angle import LARS, LassoLARS, lars_path
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from .coordinate_descent import Lasso, ElasticNet, LassoCV, ElasticNetCV, \
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lasso_path, enet_path
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from .stochastic_gradient import SGDClassifier, SGDRegressor
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from .ridge import Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV, \
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ridge_regression
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from .logistic import LogisticRegression
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from . import sparse
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