typos [ci skip]

ogrisel · ogrisel · commit a65f60eaeed4 · 2017-03-27T20:33:55.000+02:00
diff --git a/doc/modules/linear_model.rst b/doc/modules/linear_model.rst
@@ -759,8 +759,8 @@ Multinomial loss                   "lbfgs", "sag", "saga" or "newton-cg"
 Very Large dataset (`n_samples`)   "sag" or "saga"
 =================================  =====================================
 
-The "saga" solver is almost always a the best choice. The "liblinear"
-solver is used by default for historical reasons.
+The "saga" solver is often the best choice. The "liblinear" solver is
+used by default for historical reasons.
 
 For large dataset, you may also consider using :class:`SGDClassifier`
 with 'log' loss.
diff --git a/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py b/examples/linear_model/plot_sparse_logistic_regression_20newsgroups.py
@@ -8,7 +8,7 @@
 regression yields more accurate results and is faster to train on the larger
 scale dataset.
 
-Here we use the l1 sparsity that trims the weights of no to informative
+Here we use the l1 sparsity that trims the weights of not informative
 features to zero. This is good if the goal is to extract the strongly
 discriminative vocabulary of each class. If the goal is to get the best
 predictive accuracy, it is better to use the non sparsity-inducing l2 penalty
diff --git a/sklearn/linear_model/logistic.py b/sklearn/linear_model/logistic.py
@@ -977,7 +977,7 @@ class LogisticRegression(BaseEstimator, LinearClassifierMixin,
         'sag' and 'lbfgs' solvers support only l2 penalties.
 
         .. versionadded:: 0.19
-           l1 penalty with SAGA solver (allowing 'multinomial + L1)
+           l1 penalty with SAGA solver (allowing 'multinomial' + L1)
 
     dual : bool, default: False
         Dual or primal formulation. Dual formulation is only implemented for
diff --git a/sklearn/linear_model/sag_fast.pyx b/sklearn/linear_model/sag_fast.pyx
@@ -4,6 +4,7 @@
 #
 # Authors: Danny Sullivan <dbsullivan23@gmail.com>
 #          Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>
+#          Arthur Mensch <arthur.mensch@m4x.org
 #
 # License: BSD 3 clause
 cimport numpy as np
diff --git a/sklearn/linear_model/tests/test_logistic.py b/sklearn/linear_model/tests/test_logistic.py
@@ -896,7 +896,7 @@ def test_logreg_intercept_scaling_zero():
 
 
 def test_logreg_l1():
-    # Because liblinear penalizes the intercept and saga does not, we do
+    # Because liblinear penalizes the intercept and saga does not, we do not
     # fit the intercept to make it possible to compare the coefficients of
     # the two models at convergence.
     rng = np.random.RandomState(42)
@@ -924,7 +924,7 @@ def test_logreg_l1():
 
 
 def test_logreg_l1_sparse_data():
-    # Because liblinear penalizes the intercept and saga does not, we do
+    # Because liblinear penalizes the intercept and saga does not, we do not
     # fit the intercept to make it possible to compare the coefficients of
     # the two models at convergence.
     rng = np.random.RandomState(42)

Original file line number	Diff line number	Diff line change
`@@ -4,6 +4,7 @@`
`4`	`4`	`#`
`5`	`5`	`# Authors: Danny Sullivan <dbsullivan23@gmail.com>`
`6`	`6`	`# Tom Dupre la Tour <tom.dupre-la-tour@m4x.org>`
	`7`	`+# Arthur Mensch <arthur.mensch@m4x.org`
`7`	`8`	`#`
`8`	`9`	`# License: BSD 3 clause`
`9`	`10`	`cimport numpy as np`