30 lines
1.3 KiB
Python
30 lines
1.3 KiB
Python
"""
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The :mod:`sklearn.linear_model` module implements genelarized linear models. It
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includes Ridge regression, Bayesian Regression, Lasso and Elastic Net
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estimators computed with Least Angle Regression and coordinate descent. It also
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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
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from .bayes import BayesianRidge, ARDRegression
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from .least_angle import Lars, LassoLars, lars_path, LARS, LassoLARS, \
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LarsCV, LassoLarsCV, LassoLarsIC
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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 .sgd_fast import Hinge, Log, ModifiedHuber, SquaredLoss, Huber
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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 .omp import orthogonal_mp, orthogonal_mp_gram, OrthogonalMatchingPursuit
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from .perceptron import Perceptron
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from .randomized_l1 import RandomizedLasso, RandomizedLogisticRegression, \
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lasso_stability_path
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from . import sparse
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