diff --git a/doc/modules/cross_validation.rst b/doc/modules/cross_validation.rst
index 5bb98b3b4c0841f98af784a395849617c4fa8f2c..25f261592f2d528c7b6082a2d8e230eb32239908 100644
--- a/doc/modules/cross_validation.rst
+++ b/doc/modules/cross_validation.rst
@@ -40,7 +40,7 @@ data for testing (evaluating) our classifier::
>>> X_test.shape, y_test.shape
((60, 4), (60,))
- >>> clf = svm.SVC(kernel='linear', C=100).fit(X_train, y_train)
+ >>> clf = svm.SVC(kernel='linear', C=1).fit(X_train, y_train)
>>> clf.score(X_test, y_test) # doctest: +ELLIPSIS
0.96...
@@ -68,12 +68,12 @@ linear kernel Support Vector Machine on the iris dataset by splitting
the data and fitting a model and computing the score 5 consecutive times
(with different splits each time)::
- >>> clf = svm.SVC(kernel='linear', C=100)
+ >>> clf = svm.SVC(kernel='linear', C=1)
>>> scores = cross_validation.cross_val_score(
... clf, iris.data, iris.target, cv=5)
...
>>> scores # doctest: +ELLIPSIS
- array([ 1. ..., 0.96..., 0.9 ..., 0.96..., 1. ...])
+ array([ 1. ..., 0.96..., 0.9 ..., 0.96..., 1. ])
The mean score and the standard deviation of the score estimate are hence given
by::
@@ -89,7 +89,7 @@ scoring function, e.g. from the metrics module::
>>> cross_validation.cross_val_score(clf, iris.data, iris.target, cv=5,
... score_func=metrics.f1_score)
... # doctest: +ELLIPSIS
- array([ 1. ..., 0.96..., 0.89..., 0.96..., 1. ...])
+ array([ 1. ..., 0.96..., 0.89..., 0.96..., 1. ])
In the case of the Iris dataset, the samples are balanced across target
classes hence the accuracy and the F1-score are almost equal.
@@ -107,7 +107,7 @@ validation iterator instead, for instance::
>>> cross_validation.cross_val_score(clf, iris.data, iris.target, cv=cv)
... # doctest: +ELLIPSIS
- array([ 0.97..., 1. , 1. ])
+ array([ 0.97..., 0.97..., 1. ])
The available cross validation iterators are introduced in the following.
diff --git a/doc/modules/feature_selection.rst b/doc/modules/feature_selection.rst
index 6c0993733b9c9b47a3d39df6dcabf84ab0ea4e57..e4799a6b300fdd6fa6d147b31522508a83c875bb 100644
--- a/doc/modules/feature_selection.rst
+++ b/doc/modules/feature_selection.rst
@@ -97,7 +97,7 @@ for classification::
>>> X, y = iris.data, iris.target
>>> X.shape
(150, 4)
- >>> X_new = LinearSVC(C=1., penalty="l1", dual=False).fit_transform(X, y)
+ >>> X_new = LinearSVC(C=0.01, penalty="l1", dual=False).fit_transform(X, y)
>>> X_new.shape
(150, 3)
diff --git a/doc/modules/multiclass.rst b/doc/modules/multiclass.rst
index ab0e8b9f301d7ce27db6852f95ba45398609233e..fd75ac1af3768508ae75d7ba42d54158427b13c3 100644
--- a/doc/modules/multiclass.rst
+++ b/doc/modules/multiclass.rst
@@ -64,7 +64,7 @@ default choice. Below is an example::
>>> from sklearn.svm import LinearSVC
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
- >>> OneVsRestClassifier(LinearSVC(C=100.)).fit(X, y).predict(X)
+ >>> OneVsRestClassifier(LinearSVC(C=1)).fit(X, y).predict(X)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
@@ -110,7 +110,7 @@ dataset is used `n_classes` times. Below is an example::
>>> from sklearn.svm import LinearSVC
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
- >>> OneVsOneClassifier(LinearSVC(C=100.)).fit(X, y).predict(X)
+ >>> OneVsOneClassifier(LinearSVC(C=1)).fit(X, y).predict(X)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
@@ -162,7 +162,7 @@ Example::
>>> from sklearn.svm import LinearSVC
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
- >>> OutputCodeClassifier(LinearSVC(C=100.), code_size=2, random_state=0).fit(X, y).predict(X)
+ >>> OutputCodeClassifier(LinearSVC(C=1), code_size=2, random_state=0).fit(X, y).predict(X)
array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,
diff --git a/doc/tutorial/basic/tutorial.rst b/doc/tutorial/basic/tutorial.rst
index f708125e16ce8d573ccc716192e5ffcf34adaeb9..f26288cebb62ab9040b3063b6acc84b782a96682 100644
--- a/doc/tutorial/basic/tutorial.rst
+++ b/doc/tutorial/basic/tutorial.rst
@@ -167,8 +167,8 @@ one::
>>> clf.fit(digits.data[:-1], digits.target[:-1])
SVC(C=100.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
- gamma=0.001, kernel='rbf', probability=False shrinking=True, tol=0.001,
- verbose=False)
+ gamma=0.001, kernel='rbf', probability=False, shrinking=True, tol=0.001,
+ verbose=False)
Now you can predict new values, in particular, we can ask to the
classifier what is the digit of our last image in the `digits` dataset,
@@ -204,9 +204,9 @@ persistence model, namely `pickle <http://docs.python.org/library/pickle.html>`_
>>> iris = datasets.load_iris()
>>> X, y = iris.data, iris.target
>>> clf.fit(X, y)
- SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
- gamma=0.25, kernel='rbf', probability=False, shrinking=True, tol=0.001,
- verbose=False)
+ SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.25,
+ kernel='rbf', probability=False, shrinking=True, tol=0.001,
+ verbose=False)
>>> import pickle
>>> s = pickle.dumps(clf)
diff --git a/doc/tutorial/statistical_inference/model_selection.rst b/doc/tutorial/statistical_inference/model_selection.rst
index 16513bd9916b06aac98313df1ac89369e96b73da..88c56aebcefab6e169442332ab270563e964bf1b 100644
--- a/doc/tutorial/statistical_inference/model_selection.rst
+++ b/doc/tutorial/statistical_inference/model_selection.rst
@@ -19,7 +19,7 @@ better**.
>>> y_digits = digits.target
>>> svc = svm.SVC(C=1, kernel='linear')
>>> svc.fit(X_digits[:-100], y_digits[:-100]).score(X_digits[-100:], y_digits[-100:])
- 0.95999999999999996
+ 0.97999999999999998
To get a better measure of prediction accuracy (which we can use as a
proxy for goodness of fit of the model), we can successively split the
@@ -39,7 +39,7 @@ data in *folds* that we use for training and testing::
... y_train = np.concatenate(y_train)
... scores.append(svc.fit(X_train, y_train).score(X_test, y_test))
>>> print scores
- [0.94156928213689484, 0.96661101836393992, 0.93322203672787984]
+ [0.93489148580968284, 0.95659432387312182, 0.93989983305509184]
.. currentmodule:: sklearn.cross_validation
@@ -69,13 +69,13 @@ The cross-validation can then be implemented easily::
>>> kfold = cross_validation.KFold(len(X_digits), k=3)
>>> [svc.fit(X_digits[train], y_digits[train]).score(X_digits[test], y_digits[test])
... for train, test in kfold]
- [0.94156928213689484, 0.96661101836393992, 0.93322203672787984]
+ [0.93489148580968284, 0.95659432387312182, 0.93989983305509184]
To compute the `score` method of an estimator, the sklearn exposes
a helper function::
>>> cross_validation.cross_val_score(svc, X_digits, y_digits, cv=kfold, n_jobs=-1)
- array([ 0.94156928, 0.96661102, 0.93322204])
+ array([ 0.93489149, 0.95659432, 0.93989983])
`n_jobs=-1` means that the computation will be dispatched on all the CPUs
of the computer.
@@ -146,13 +146,13 @@ estimator during the construction and exposes an estimator API::
>>> clf.fit(X_digits[:1000], y_digits[:1000]) # doctest: +ELLIPSIS
GridSearchCV(cv=None,...
>>> clf.best_score_
- 0.98699897502292733
+ 0.988991985997974
>>> clf.best_estimator_.gamma
9.9999999999999995e-07
>>> # Prediction performance on test set is not as good as on train set
>>> clf.score(X_digits[1000:], y_digits[1000:])
- 0.94353826850690092
+ 0.94228356336260977
By default the :class:`GridSearchCV` uses a 3-fold cross-validation. However, if
@@ -164,7 +164,7 @@ a stratified 3-fold.
::
>>> cross_validation.cross_val_score(clf, X_digits, y_digits)
- array([ 0.98497496, 0.97829716, 0.97996661])
+ array([ 0.97996661, 0.98163606, 0.98330551])
Two cross-validation loops are performed in parallel: one by the
:class:`GridSearchCV` estimator to set `gamma`, the other one by
diff --git a/doc/tutorial/statistical_inference/supervised_learning.rst b/doc/tutorial/statistical_inference/supervised_learning.rst
index 08dc5d4d67ec612c5783ab0949117a123af7d034..b79d0d8ff7b08283a851fc99cb9c60e50212edd8 100644
--- a/doc/tutorial/statistical_inference/supervised_learning.rst
+++ b/doc/tutorial/statistical_inference/supervised_learning.rst
@@ -450,9 +450,9 @@ classification --:class:`SVC` (Support Vector Classification).
>>> from sklearn import svm
>>> svc = svm.SVC(kernel='linear')
>>> svc.fit(iris_X_train, iris_y_train)
- SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3,
- gamma=0.0, kernel='linear', probability=False, shrinking=True, tol=0.001,
- verbose=False)
+ SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0,
+ kernel='linear', probability=False, shrinking=True, tol=0.001,
+ verbose=False)
.. warning:: **Normalizing data**