scikit-learn/sklearn/linear_model/__init__.py

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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
estimators computed with Least Angle Regression and coordinate descent. It also
implements Stochastic Gradient Descent related algorithms.
"""
# See http://scikit-learn.sourceforge.net/modules/sgd.html and
# http://scikit-learn.sourceforge.net/modules/linear_model.html for
# complete documentation.
from .base import LinearRegression
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from .bayes import BayesianRidge, ARDRegression
from .least_angle import Lars, LassoLars, lars_path, LarsCV, LassoLarsCV, \
LassoLarsIC
from .coordinate_descent import Lasso, ElasticNet, LassoCV, ElasticNetCV, \
lasso_path, enet_path, MultiTaskLasso, \
MultiTaskElasticNet
from .sgd_fast import Hinge, Log, ModifiedHuber, SquaredLoss, Huber
from .stochastic_gradient import SGDClassifier, SGDRegressor
from .ridge import Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV, \
ridge_regression
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
from .randomized_l1 import RandomizedLasso, RandomizedLogisticRegression, \
lasso_stability_path
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from .isotonic_regression_ import IsotonicRegression, isotonic_regression
__all__ = ['ARDRegression',
'BayesianRidge',
'ElasticNet',
'ElasticNetCV',
'Hinge',
'Huber',
'Lars',
'LarsCV',
'Lasso',
'LassoCV',
'LassoLars',
'LassoLarsCV',
'LassoLarsIC',
'LinearRegression',
'Log',
'LogisticRegression',
'ModifiedHuber',
'MultiTaskElasticNet',
'MultiTaskLasso',
'OrthogonalMatchingPursuit',
'Perceptron',
'RandomizedLasso',
'RandomizedLogisticRegression',
'Ridge',
'RidgeCV',
'RidgeClassifier',
'RidgeClassifierCV',
'SGDClassifier',
'SGDRegressor',
'SquaredLoss',
'enet_path',
'lars_path',
'lasso_path',
'lasso_stability_path',
'orthogonal_mp',
'orthogonal_mp_gram',
'ridge_regression']