From 3d1b786546a033acbafe2ed32d7a008b8e659e5f Mon Sep 17 00:00:00 2001
From: srajan paliwal <srajanpaliwal@gmail.com>
Date: Fri, 27 Oct 2017 07:10:47 -0400
Subject: [PATCH] [MRG] Fix LogisticRegression see also should include
LogisticRegressionCV(#9995) (#10022)
---
sklearn/calibration.py | 4 ++++
sklearn/feature_selection/rfe.py | 9 +++++++++
sklearn/linear_model/coordinate_descent.py | 11 +++++++++--
sklearn/linear_model/least_angle.py | 1 +
sklearn/linear_model/logistic.py | 1 +
sklearn/linear_model/omp.py | 2 +-
sklearn/linear_model/ridge.py | 20 ++++++++++++--------
7 files changed, 37 insertions(+), 11 deletions(-)
diff --git a/sklearn/calibration.py b/sklearn/calibration.py
index 0d2f76cd12..3c09d5c02f 100644
--- a/sklearn/calibration.py
+++ b/sklearn/calibration.py
@@ -265,6 +265,10 @@ class _CalibratedClassifier(object):
if None, then classes is extracted from the given target values
in fit().
+ See also
+ --------
+ CalibratedClassifierCV
+
References
----------
.. [1] Obtaining calibrated probability estimates from decision trees
diff --git a/sklearn/feature_selection/rfe.py b/sklearn/feature_selection/rfe.py
index 1b95c92fdb..5bde9e57c3 100644
--- a/sklearn/feature_selection/rfe.py
+++ b/sklearn/feature_selection/rfe.py
@@ -101,6 +101,11 @@ class RFE(BaseEstimator, MetaEstimatorMixin, SelectorMixin):
>>> selector.ranking_
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
+ See also
+ --------
+ RFECV : Recursive feature elimination with built-in cross-validated
+ selection of the best number of features
+
References
----------
@@ -365,6 +370,10 @@ class RFECV(RFE, MetaEstimatorMixin):
>>> selector.ranking_
array([1, 1, 1, 1, 1, 6, 4, 3, 2, 5])
+ See also
+ --------
+ RFE : Recursive feature elimination
+
References
----------
diff --git a/sklearn/linear_model/coordinate_descent.py b/sklearn/linear_model/coordinate_descent.py
index e03aece7f2..388c6ca49b 100644
--- a/sklearn/linear_model/coordinate_descent.py
+++ b/sklearn/linear_model/coordinate_descent.py
@@ -640,6 +640,8 @@ class ElasticNet(LinearModel, RegressorMixin):
See also
--------
+ ElasticNetCV : Elastic net model with best model selection by
+ cross-validation.
SGDRegressor: implements elastic net regression with incremental training.
SGDClassifier: implements logistic regression with elastic net penalty
(``SGDClassifier(loss="log", penalty="elasticnet")``).
@@ -1688,7 +1690,10 @@ class MultiTaskElasticNet(Lasso):
See also
--------
- ElasticNet, MultiTaskLasso
+ MultiTaskElasticNet : Multi-task L1/L2 ElasticNet with built-in
+ cross-validation.
+ ElasticNet
+ MultiTaskLasso
Notes
-----
@@ -1873,7 +1878,9 @@ class MultiTaskLasso(MultiTaskElasticNet):
See also
--------
- Lasso, MultiTaskElasticNet
+ MultiTaskLasso : Multi-task L1/L2 Lasso with built-in cross-validation
+ Lasso
+ MultiTaskElasticNet
Notes
-----
diff --git a/sklearn/linear_model/least_angle.py b/sklearn/linear_model/least_angle.py
index bb7c12ab60..88fae8aa72 100644
--- a/sklearn/linear_model/least_angle.py
+++ b/sklearn/linear_model/least_angle.py
@@ -824,6 +824,7 @@ class LassoLars(Lars):
Lasso
LassoCV
LassoLarsCV
+ LassoLarsIC
sklearn.decomposition.sparse_encode
"""
diff --git a/sklearn/linear_model/logistic.py b/sklearn/linear_model/logistic.py
index 7c8a8d9ae4..3de13a86b5 100644
--- a/sklearn/linear_model/logistic.py
+++ b/sklearn/linear_model/logistic.py
@@ -1120,6 +1120,7 @@ class LogisticRegression(BaseEstimator, LinearClassifierMixin,
SGDClassifier : incrementally trained logistic regression (when given
the parameter ``loss="log"``).
sklearn.svm.LinearSVC : learns SVM models using the same algorithm.
+ LogisticRegressionCV : Logistic regression with built-in cross validation
Notes
-----
diff --git a/sklearn/linear_model/omp.py b/sklearn/linear_model/omp.py
index 8fcbd4e211..9870105580 100644
--- a/sklearn/linear_model/omp.py
+++ b/sklearn/linear_model/omp.py
@@ -598,7 +598,7 @@ class OrthogonalMatchingPursuit(LinearModel, RegressorMixin):
Lars
LassoLars
decomposition.sparse_encode
-
+ OrthogonalMatchingPursuitCV
"""
def __init__(self, n_nonzero_coefs=None, tol=None, fit_intercept=True,
normalize=True, precompute='auto'):
diff --git a/sklearn/linear_model/ridge.py b/sklearn/linear_model/ridge.py
index 8a48cef65c..c46cdff7da 100644
--- a/sklearn/linear_model/ridge.py
+++ b/sklearn/linear_model/ridge.py
@@ -624,7 +624,10 @@ class Ridge(_BaseRidge, RegressorMixin):
See also
--------
- RidgeClassifier, RidgeCV, :class:`sklearn.kernel_ridge.KernelRidge`
+ RidgeClassifier : Ridge classifier
+ RidgeCV : Ridge regression with built-in cross validation
+ :class:`sklearn.kernel_ridge.KernelRidge` : Kernel ridge regression
+ combines ridge regression with the kernel trick
Examples
--------
@@ -770,7 +773,8 @@ class RidgeClassifier(LinearClassifierMixin, _BaseRidge):
See also
--------
- Ridge, RidgeClassifierCV
+ Ridge : Ridge regression
+ RidgeClassifierCV : Ridge classifier with built-in cross validation
Notes
-----
@@ -1233,9 +1237,9 @@ class RidgeCV(_BaseRidgeCV, RegressorMixin):
See also
--------
- Ridge: Ridge regression
- RidgeClassifier: Ridge classifier
- RidgeClassifierCV: Ridge classifier with built-in cross validation
+ Ridge : Ridge regression
+ RidgeClassifier : Ridge classifier
+ RidgeClassifierCV : Ridge classifier with built-in cross validation
"""
pass
@@ -1318,9 +1322,9 @@ class RidgeClassifierCV(LinearClassifierMixin, _BaseRidgeCV):
See also
--------
- Ridge: Ridge regression
- RidgeClassifier: Ridge classifier
- RidgeCV: Ridge regression with built-in cross validation
+ Ridge : Ridge regression
+ RidgeClassifier : Ridge classifier
+ RidgeCV : Ridge regression with built-in cross validation
Notes
-----
--
GitLab