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diff --git a/‎dev/_sources/about.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.txt
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@@ -75,6 +75,7 @@ full-time. It also hosts coding sprints and other events.
 .. image:: images/inria-logo.jpg
    :width: 200pt
    :align: center
+   :target: https://www.inria.fr
 
 `Paris-Saclay Center for Data Science <http://www.datascience-paris-saclay.fr>`_
 funded one year for a developer to work on the project full-time
@@ -83,6 +84,7 @@ funded one year for a developer to work on the project full-time
 .. image:: images/cds-logo.png
    :width: 200pt
    :align: center
+   :target: http://www.datascience-paris-saclay.fr
 
 `NYU Moore-Sloan Data Science Environment <http://cds.nyu.edu/mooresloan/>`_
 funds Andreas Mueller (2014-2015) to work on this project. The Moore-Sloan Data Science
@@ -91,6 +93,16 @@ Environment also funds several students to work on the project part-time.
 .. image:: images/nyu_short_color.png
    :width: 200pt
    :align: center
+   :target: http://cds.nyu.edu/mooresloan/
+
+`Télécom Paristech <http://www.telecom-paristech.com>`_ funds Manoj Kumar (2014),
+Tom Dupré la Tour (2015), Raghav R V (2015-2016) and Thierry Guillemot (2016) to
+work on scikit-learn.
+
+.. image:: themes/scikit-learn/static/img/telecom.png
+   :width: 100pt
+   :align: center
+   :target: http://www.telecom-paristech.fr/
 
 The following students were sponsored by `Google <https://developers.google.com/open-source/>`_
 to work on scikit-learn through the
@@ -177,7 +189,7 @@ The 2013 Paris international sprint
     <div style="text-align: center; margin: -7px 0 -10px 0;">
 
 
-.. |telecom| image:: http://f.hypotheses.org/wp-content/blogs.dir/331/files/2011/03/Logo-TPT.jpg
+.. |telecom| image:: themes/scikit-learn/static/img/telecom.png
    :width: 120pt
    :target: http://www.telecom-paristech.fr/
 
@@ -226,4 +238,3 @@ Infrastructure support
 - We would also like to thank `Shining Panda
   <http://shiningpanda.com/>`_ for free CPU time on their Continuous
   Integration server.
-
 
@@ -226,69 +226,69 @@ main code
            learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1,
            penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True,
            verbose=0, warm_start=False)
-    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.027064s
+    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.027466s
 
     Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
            eta0=0.0, fit_intercept=True, l1_ratio=0.5, learning_rate='optimal',
            loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet',
            power_t=0.5, random_state=None, shuffle=True, verbose=0,
            warm_start=False)
-    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.020459s
+    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.020418s
 
     Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
            eta0=0.0, fit_intercept=True, l1_ratio=0.75,
            learning_rate='optimal', loss='modified_huber', n_iter=5, n_jobs=1,
            penalty='elasticnet', power_t=0.5, random_state=None, shuffle=True,
            verbose=0, warm_start=False)
-    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.016890s
+    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.016507s
 
     Benchmarking SGDClassifier(alpha=0.001, average=False, class_weight=None, epsilon=0.1,
            eta0=0.0, fit_intercept=True, l1_ratio=0.9, learning_rate='optimal',
            loss='modified_huber', n_iter=5, n_jobs=1, penalty='elasticnet',
            power_t=0.5, random_state=None, shuffle=True, verbose=0,
            warm_start=False)
-    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.015149s
+    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.014494s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.1, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000366s
+    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000393s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.25, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000660s
+    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000698s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.5, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001113s
+    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001200s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.75, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001563s
+    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001689s
 
     Benchmarking NuSVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, gamma=3.0517578125e-05,
        kernel='rbf', max_iter=-1, nu=0.9, shrinking=True, tol=0.001,
        verbose=False)
-    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001791s
+    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001925s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
                  max_leaf_nodes=None, min_impurity_split=1e-07,
                  min_samples_leaf=1, min_samples_split=2,
                  min_weight_fraction_leaf=0.0, n_estimators=10, presort='auto',
                  random_state=None, subsample=1.0, verbose=0, warm_start=False)
-    Complexity: 10 | MSE: 28.9793 | Pred. Time: 0.000110s
+    Complexity: 10 | MSE: 28.9793 | Pred. Time: 0.000118s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
                  max_leaf_nodes=None, min_impurity_split=1e-07,
                  min_samples_leaf=1, min_samples_split=2,
                  min_weight_fraction_leaf=0.0, n_estimators=50, presort='auto',
                  random_state=None, subsample=1.0, verbose=0, warm_start=False)
-    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000196s
+    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000209s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -297,7 +297,7 @@ main code
                  min_weight_fraction_leaf=0.0, n_estimators=100,
                  presort='auto', random_state=None, subsample=1.0, verbose=0,
                  warm_start=False)
-    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000282s
+    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000302s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -306,7 +306,7 @@ main code
                  min_weight_fraction_leaf=0.0, n_estimators=200,
                  presort='auto', random_state=None, subsample=1.0, verbose=0,
                  warm_start=False)
-    Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000449s
+    Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000478s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -315,10 +315,10 @@ main code
                  min_weight_fraction_leaf=0.0, n_estimators=500,
                  presort='auto', random_state=None, subsample=1.0, verbose=0,
                  warm_start=False)
-    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.000973s
+    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.001048s
 
 
-**Total running time of the script:** ( 0 minutes  24.481 seconds)
+**Total running time of the script:** ( 0 minutes  25.290 seconds)
 
 
 
 
@@ -356,46 +356,46 @@ maximum
  Out::
 
     Test set is 878 documents (108 positive)
-      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.913 in 1.61s (  598 docs/s)
-              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.61s (  596 docs/s)
-                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.62s (  595 docs/s)
-          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.65s (  582 docs/s)
+      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.913 in 1.75s (  550 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.75s (  549 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.75s (  548 docs/s)
+          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.79s (  537 docs/s)
 
 
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 4.70s (  832 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 4.70s (  831 docs/s)
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 4.71s (  830 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 4.74s (  824 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 5.13s (  763 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.13s (  762 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 5.13s (  761 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.17s (  756 docs/s)
 
 
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 7.87s (  867 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 7.87s (  866 docs/s)
-                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 7.88s (  866 docs/s)
-          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 7.91s (  862 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 8.51s (  801 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 8.51s (  801 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 8.52s (  800 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 8.55s (  797 docs/s)
 
 
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 11.02s (  885 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 11.02s (  885 docs/s)
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 11.03s (  885 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 11.06s (  882 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 11.88s (  821 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 11.88s (  821 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 11.89s (  821 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 11.92s (  818 docs/s)
 
 
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.81s (  845 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.81s (  845 docs/s)
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.82s (  845 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 13.85s (  843 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.90s (  783 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.90s (  783 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.91s (  783 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 14.94s (  781 docs/s)
 
 
-      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 16.96s (  862 docs/s)
-              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 16.96s (  862 docs/s)
-                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 16.97s (  861 docs/s)
-          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 17.00s (  860 docs/s)
+      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 18.28s (  800 docs/s)
+              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 18.28s (  800 docs/s)
+                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 18.28s (  799 docs/s)
+          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 18.32s (  798 docs/s)
 
 
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 19.93s (  871 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 19.93s (  870 docs/s)
-                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 19.94s (  870 docs/s)
-          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 19.97s (  869 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 21.43s (  810 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 21.43s (  809 docs/s)
+                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 21.44s (  809 docs/s)
+          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 21.47s (  808 docs/s)
 
 
 Plot results
@@ -526,7 +526,7 @@ Plot results
 
 
 
-**Total running time of the script:** ( 0 minutes  21.841 seconds)
+**Total running time of the script:** ( 0 minutes  23.420 seconds)
 
 
 
 
@@ -158,7 +158,7 @@ risk of over-fitting the data.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  4.373 seconds)
+**Total running time of the script:** ( 0 minutes  4.683 seconds)
 
 
 
 
@@ -372,10 +372,10 @@ main code
     benchmarking with 100 features
     benchmarking with 250 features
     benchmarking with 500 features
-    example run in 3.71s
+    example run in 3.87s
 
 
-**Total running time of the script:** ( 0 minutes  3.708 seconds)
+**Total running time of the script:** ( 0 minutes  3.875 seconds)
 
 
 
 
@@ -65,7 +65,7 @@ References
 
      Area under the ROC curve : 0.993919
 
-    time elapsed: 7.09s
+    time elapsed: 7.01s
 
 
 
@@ -247,7 +247,7 @@ References
     plot_species_distribution()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  7.089 seconds)
+**Total running time of the script:** ( 0 minutes  7.012 seconds)
 
 
 
 
@@ -340,7 +340,7 @@ Visualization
 
 
 
-**Total running time of the script:** ( 0 minutes  9.742 seconds)
+**Total running time of the script:** ( 0 minutes  10.355 seconds)
 
 
 
 
@@ -164,7 +164,7 @@ contributed to fewer projections than the central disk.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  9.252 seconds)
+**Total running time of the script:** ( 0 minutes  9.886 seconds)
 
 
 
 
@@ -106,7 +106,7 @@ representation of the checkerboard structure.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.875 seconds)
+**Total running time of the script:** ( 0 minutes  0.950 seconds)
 
 
 
 
@@ -93,7 +93,7 @@ the biclusters.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.221 seconds)
+**Total running time of the script:** ( 0 minutes  0.241 seconds)
 
 
 
 
@@ -159,7 +159,7 @@ Plot the data and the predicted probabilities
 
 
 
-**Total running time of the script:** ( 0 minutes  0.264 seconds)
+**Total running time of the script:** ( 0 minutes  0.284 seconds)
 
 
 
 
@@ -214,7 +214,7 @@ but not where it is transposed-sigmoid (e.g., Gaussian naive Bayes).
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  2.160 seconds)
+**Total running time of the script:** ( 0 minutes  2.270 seconds)
 
 
 
 
@@ -204,7 +204,7 @@ would have resulted in a similar decrease in log-loss.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.662 seconds)
+**Total running time of the script:** ( 0 minutes  0.711 seconds)
 
 
 
 
@@ -147,7 +147,7 @@ Plot calibration plots
 
 
 
-**Total running time of the script:** ( 0 minutes  2.545 seconds)
+**Total running time of the script:** ( 0 minutes  2.693 seconds)
 
 
 
 
@@ -113,7 +113,7 @@ a result it can identify only the first class.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  2.447 seconds)
+**Total running time of the script:** ( 0 minutes  2.578 seconds)
 
 
 
 
@@ -153,7 +153,7 @@ set.
     plt.tight_layout()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  7.662 seconds)
+**Total running time of the script:** ( 0 minutes  8.091 seconds)
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`@@ -158,7 +158,7 @@ risk of over-fitting the data.`
`158`	`158`
`159`	`159`	`plt.show()`
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`161`		`-Total running time of the script: ( 0 minutes 4.373 seconds)`
	`161`	`+Total running time of the script: ( 0 minutes 4.683 seconds)`
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`@@ -340,7 +340,7 @@ Visualization`
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`343`		`-Total running time of the script: ( 0 minutes 9.742 seconds)`
	`343`	`+Total running time of the script: ( 0 minutes 10.355 seconds)`
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`@@ -164,7 +164,7 @@ contributed to fewer projections than the central disk.`
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`165`	`165`	`plt.show()`
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`167`		`-Total running time of the script: ( 0 minutes 9.252 seconds)`
	`167`	`+Total running time of the script: ( 0 minutes 9.886 seconds)`
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`@@ -106,7 +106,7 @@ representation of the checkerboard structure.`
`106`	`106`
`107`	`107`	`plt.show()`
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`109`		`-Total running time of the script: ( 0 minutes 0.875 seconds)`
	`109`	`+Total running time of the script: ( 0 minutes 0.950 seconds)`
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`@@ -93,7 +93,7 @@ the biclusters.`
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`94`	`94`	`plt.show()`
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`96`		`-Total running time of the script: ( 0 minutes 0.221 seconds)`
	`96`	`+Total running time of the script: ( 0 minutes 0.241 seconds)`
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`@@ -159,7 +159,7 @@ Plot the data and the predicted probabilities`
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`162`		`-Total running time of the script: ( 0 minutes 0.264 seconds)`
	`162`	`+Total running time of the script: ( 0 minutes 0.284 seconds)`
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`@@ -214,7 +214,7 @@ but not where it is transposed-sigmoid (e.g., Gaussian naive Bayes).`
`214`	`214`
`215`	`215`	`plt.show()`
`216`	`216`
`217`		`-Total running time of the script: ( 0 minutes 2.160 seconds)`
	`217`	`+Total running time of the script: ( 0 minutes 2.270 seconds)`
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`@@ -204,7 +204,7 @@ would have resulted in a similar decrease in log-loss.`
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`205`	`205`	`plt.show()`
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`207`		`-Total running time of the script: ( 0 minutes 0.662 seconds)`
	`207`	`+Total running time of the script: ( 0 minutes 0.711 seconds)`
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`@@ -147,7 +147,7 @@ Plot calibration plots`
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`148`	`148`
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`150`		`-Total running time of the script: ( 0 minutes 2.545 seconds)`
	`150`	`+Total running time of the script: ( 0 minutes 2.693 seconds)`
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`@@ -113,7 +113,7 @@ a result it can identify only the first class.`
`113`	`113`
`114`	`114`	`plt.show()`
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`116`		`-Total running time of the script: ( 0 minutes 2.447 seconds)`
	`116`	`+Total running time of the script: ( 0 minutes 2.578 seconds)`
`117`	`117`
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`119`	`119`