Pushing the docs for revision for branch: master, commit 24198ffbb8f3d251e867ce299a42192dcad5fa82

Author: sklearn-ci

dev/_downloads/plot_calibration.py

@@ -11,7 +11,7 @@
 probabilities is often desirable as a postprocessing. This example illustrates
 two different methods for this calibration and evaluates the quality of the
 returned probabilities using Brier's score
-(see http://en.wikipedia.org/wiki/Brier_score).
+(see https://en.wikipedia.org/wiki/Brier_score).
 
 Compared are the estimated probability using a Gaussian naive Bayes classifier
 without calibration, with a sigmoid calibration, and with a non-parametric

dev/_downloads/plot_iris_dataset.py

@@ -13,7 +13,7 @@
 Sepal Length, Sepal Width, Petal Length	and Petal Width.
 
 The below plot uses the first two features.
-See `here <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
+See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
 information on this dataset.
 """
 print(__doc__)

dev/_downloads/plot_iris_logistic.py

@@ -7,7 +7,7 @@
 =========================================================
 
 Show below is a logistic-regression classifiers decision boundaries on the
-`iris <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_ dataset. The
+`iris <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ dataset. The
 datapoints are colored according to their labels.
 
 """

dev/_downloads/plot_johnson_lindenstrauss_bound.py

@@ -8,7 +8,7 @@
 dataset can be randomly projected into a lower dimensional Euclidean
 space while controlling the distortion in the pairwise distances.
 
-.. _`Johnson-Lindenstrauss lemma`: http://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma
+.. _`Johnson-Lindenstrauss lemma`: https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma
 
 
 Theoretical bounds

dev/_downloads/plot_manifold_sphere.py

@@ -24,7 +24,7 @@
 it does not seeks an isotropic representation of the data in
 the low-dimensional space. Here the manifold problem matches fairly
 that of representing a flat map of the Earth, as with
-`map projection <http://en.wikipedia.org/wiki/Map_projection>`_
+`map projection <https://en.wikipedia.org/wiki/Map_projection>`_
 """
 
 # Author: Jaques Grobler <[email protected]>

dev/_downloads/plot_pca_iris.py

@@ -8,7 +8,7 @@
 
 Principal Component Analysis applied to the Iris dataset.
 
-See `here <http://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
+See `here <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_ for more
 information on this dataset.
 
 """

dev/_downloads/plot_species_distribution_modeling.py

@@ -13,17 +13,17 @@
 by the package `sklearn.svm` as our modeling tool.
 The dataset is provided by Phillips et. al. (2006).
 If available, the example uses
-`basemap <http://matplotlib.sourceforge.net/basemap/doc/html/>`_
+`basemap <http://matplotlib.org/basemap>`_
 to plot the coast lines and national boundaries of South America.
 
 The two species are:
 
  - `"Bradypus variegatus"
-   <http://www.iucnredlist.org/apps/redlist/details/3038/0>`_ ,
+   <http://www.iucnredlist.org/details/3038/0>`_ ,
    the Brown-throated Sloth.
 
  - `"Microryzomys minutus"
-   <http://www.iucnredlist.org/apps/redlist/details/13408/0>`_ ,
+   <http://www.iucnredlist.org/details/13408/0>`_ ,
    also known as the Forest Small Rice Rat, a rodent that lives in Peru,
    Colombia, Ecuador, Peru, and Venezuela.
 

dev/_downloads/plot_species_kde.py

@@ -7,7 +7,7 @@
 Haversine distance metric -- i.e. distances over points in latitude/longitude.
 The dataset is provided by Phillips et. al. (2006).
 If available, the example uses
-`basemap <http://matplotlib.sourceforge.net/basemap/doc/html/>`_
+`basemap <http://matplotlib.org/basemap>`_
 to plot the coast lines and national boundaries of South America.
 
 This example does not perform any learning over the data

dev/_downloads/wikipedia_principal_eigenvector.py

@@ -8,7 +8,7 @@
 so as to assign to each vertex the values of the components of the first
 eigenvector as a centrality score:
 
-    http://en.wikipedia.org/wiki/Eigenvector_centrality
+    https://en.wikipedia.org/wiki/Eigenvector_centrality
 
 On the graph of webpages and links those values are called the PageRank
 scores by Google.
@@ -20,7 +20,7 @@
 The traditional way to compute the principal eigenvector is to use the
 power iteration method:
 
-    http://en.wikipedia.org/wiki/Power_iteration
+    https://en.wikipedia.org/wiki/Power_iteration
 
 Here the computation is achieved thanks to Martinsson's Randomized SVD
 algorithm implemented in the scikit.