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Estimators encapsulates an algorithm with (possibly) a corresponding loss function and penalties.
Created on Sat Nov 2 15:19:17 2013
Copyright (c) 2013-2014, CEA/DSV/I2BM/Neurospin. All rights reserved.
Author: Tommy Löfstedt, Edouard Duchesnay
License: BSD 3-clause.
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BaseEstimator Base class for estimators. |
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RegressionEstimator Base estimator for regression estimation. |
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LogisticRegressionEstimator Base estimator for logistic regression estimation |
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LinearRegression Linear regression: |
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RidgeRegression Linear regression with an L2 penalty. |
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Lasso Linear regression with an L1 penalty: |
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ElasticNet Linear regression with L1 and L2 penalties. |
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LinearRegressionL1L2TV Linear regression with L1, L2 and TV penalties: |
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LinearRegressionL1L2GL Linear regression with L1, L2 and Group lasso penalties: |
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LogisticRegression Logistic regression (re-weighted log-likelihood aka. |
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RidgeLogisticRegression Logistic regression (re-weighted log-likelihood aka. |
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ElasticNetLogisticRegression Logistic regression (re-weighted log-likelihood aka. |
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LogisticRegressionL1L2TV Logistic regression (re-weighted log-likelihood aka. |
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LogisticRegressionL1L2GL Logistic regression (re-weighted log-likelihood aka. |
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LinearRegressionL2SmoothedL1TV Linear regression with L2 and simultaneously smoothed L1 and TV penalties: |
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PLSRegression Estimator for PLS regression |
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SparsePLSRegression Estimator for sparse PLS regression |
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Clustering Estimator for the clustering problem, i.e. |
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__package__ = |
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