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diff --git a/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_out_of_core_classification.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_outlier_detection_housing.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_prediction_latency.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_species_distribution_modeling.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_stock_market.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_tomography_l1_reconstruction.txt
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@@ -223,53 +223,53 @@ 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: 0.027839s
+    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.026763s
 
     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: 0.020501s
+    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.019912s
 
     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: 0.017248s
+    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.016332s
 
     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: 0.015031s
+    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.015345s
 
     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: 0.000385s
+    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000367s
 
     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: 0.000687s
+    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000648s
 
     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: 0.001162s
+    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001119s
 
     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: 0.001640s
+    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001563s
 
     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: 0.001820s
+    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001793s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -285,7 +285,7 @@ main code
                  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: 0.000186s
+    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000185s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -294,7 +294,7 @@ 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: 0.000263s
+    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000262s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -303,7 +303,7 @@ 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: 0.000414s
+    Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000409s
 
     Benchmarking GradientBoostingRegressor(alpha=0.9, criterion='friedman_mse', init=None,
                  learning_rate=0.1, loss='ls', max_depth=3, max_features=None,
@@ -312,11 +312,11 @@ 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: 0.000896s
+    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.000888s
 
 
 **Total running time of the script:**
-(0 minutes 25.416 seconds)
+(0 minutes 24.382 seconds)
 
 
 
 
@@ -353,46 +353,46 @@ 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 1.92s (  502 docs/s)
-              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.92s (  501 docs/s)
-                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.92s (  500 docs/s)
-          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.96s (  491 docs/s)
+      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.913 in 1.73s (  555 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.74s (  553 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.75s (  550 docs/s)
+          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.78s (  539 docs/s)
 
 
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 5.71s (  685 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.71s (  684 docs/s)
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 5.72s (  684 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.75s (  680 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 5.33s (  733 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.34s (  732 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 5.34s (  731 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.38s (  727 docs/s)
 
 
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 9.19s (  742 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 9.19s (  741 docs/s)
-                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 9.20s (  741 docs/s)
-          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 9.23s (  738 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 8.83s (  772 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 8.84s (  772 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 8.84s (  771 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 8.87s (  768 docs/s)
 
 
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 12.91s (  755 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 12.91s (  755 docs/s)
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 12.92s (  755 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 12.95s (  753 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 12.34s (  790 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 12.34s (  790 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 12.35s (  790 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 12.38s (  788 docs/s)
 
 
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 16.28s (  717 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 16.29s (  717 docs/s)
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 16.29s (  716 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 16.32s (  715 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 15.39s (  759 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 15.39s (  758 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 15.39s (  758 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 15.43s (  757 docs/s)
 
 
-      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 20.21s (  723 docs/s)
-              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 20.22s (  723 docs/s)
-                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 20.22s (  723 docs/s)
-          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 20.26s (  722 docs/s)
+      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 19.03s (  768 docs/s)
+              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 19.04s (  768 docs/s)
+                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 19.04s (  768 docs/s)
+          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 19.07s (  766 docs/s)
 
 
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 23.71s (  732 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 23.72s (  731 docs/s)
-                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 23.72s (  731 docs/s)
-          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 23.76s (  730 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 22.27s (  779 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 22.28s (  779 docs/s)
+                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 22.28s (  779 docs/s)
+          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 22.31s (  778 docs/s)
 
 
 Plot results
@@ -523,7 +523,7 @@ Plot results
 
 
 **Total running time of the script:**
-(0 minutes 25.829 seconds)
+(0 minutes 24.321 seconds)
 
 
 
 
@@ -158,7 +158,7 @@ risk of over-fitting the data.
     plt.show()
 
 **Total running time of the script:**
-(0 minutes 4.785 seconds)
+(0 minutes 4.390 seconds)
 
 
 
 
@@ -370,11 +370,11 @@ main code
     benchmarking with 100 features
     benchmarking with 250 features
     benchmarking with 500 features
-    example run in 3.70s
+    example run in 3.61s
 
 
 **Total running time of the script:**
-(0 minutes 3.705 seconds)
+(0 minutes 3.612 seconds)
 
 
 
 
@@ -64,7 +64,7 @@ References
 
      Area under the ROC curve : 0.993919
 
-    time elapsed: 7.19s
+    time elapsed: 6.34s
 
 
 
@@ -247,7 +247,7 @@ References
     plt.show()
 
 **Total running time of the script:**
-(0 minutes 7.194 seconds)
+(0 minutes 6.340 seconds)
 
 
 
 
@@ -335,7 +335,7 @@ Visualization
 
 
 **Total running time of the script:**
-(0 minutes 11.838 seconds)
+(0 minutes 10.473 seconds)
 
 
 
 
@@ -164,7 +164,7 @@ contributed to fewer projections than the central disk.
     plt.show()
 
 **Total running time of the script:**
-(0 minutes 9.340 seconds)
+(0 minutes 9.155 seconds)
 
 
 
 
@@ -106,7 +106,7 @@ representation of the checkerboard structure.
     plt.show()
 
 **Total running time of the script:**
-(0 minutes 0.832 seconds)
+(0 minutes 0.808 seconds)
 
 
 
 
@@ -158,7 +158,7 @@ Plot the data and the predicted probabilities
 
 
 **Total running time of the script:**
-(0 minutes 0.286 seconds)
+(0 minutes 0.263 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 2.181 seconds)
+(0 minutes 2.140 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 0.687 seconds)
+(0 minutes 0.636 seconds)
 
 
 
 
@@ -146,7 +146,7 @@ Plot calibration plots
 
 
 **Total running time of the script:**
-(0 minutes 2.544 seconds)
+(0 minutes 2.462 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)
+(0 minutes 2.432 seconds)
 
 
 
 
@@ -153,7 +153,7 @@ set.
     plt.show()
 
 **Total running time of the script:**
-(0 minutes 8.950 seconds)
+(0 minutes 7.649 seconds)
 
 
 
 
@@ -120,7 +120,7 @@ This example is commented in the
     plt.show()
 
 **Total running time of the script:**
-(0 minutes 0.813 seconds)
+(0 minutes 0.760 seconds)
 
 
 
 
@@ -86,7 +86,7 @@ Shows how shrinkage improves classification.
     plt.show()
 
 **Total running time of the script:**
-(0 minutes 5.997 seconds)
+(0 minutes 5.794 seconds)
 
 
 
 
@@ -197,7 +197,7 @@ plot functions
 
 
 **Total running time of the script:**
-(0 minutes 0.447 seconds)
+(0 minutes 0.393 seconds)
 
 
 
 
@@ -45,21 +45,21 @@ value of k on various overlapping sub-samples of the dataset.
  Out::
 
       Computing adjusted_rand_score for 10 values of n_clusters and n_samples=100
-    done in 0.047s
+    done in 0.040s
     Computing v_measure_score for 10 values of n_clusters and n_samples=100
     done in 0.062s
     Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=100
-    done in 0.505s
+    done in 0.420s
     Computing mutual_info_score for 10 values of n_clusters and n_samples=100
-    done in 0.058s
+    done in 0.049s
     Computing adjusted_rand_score for 10 values of n_clusters and n_samples=1000
-    done in 0.069s
+    done in 0.061s
     Computing v_measure_score for 10 values of n_clusters and n_samples=1000
-    done in 0.091s
+    done in 0.078s
     Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=1000
-    done in 0.315s
+    done in 0.283s
     Computing mutual_info_score for 10 values of n_clusters and n_samples=1000
-    done in 0.074s
+    done in 0.064s
 
 
 
@@ -171,7 +171,7 @@ value of k on various overlapping sub-samples of the dataset.
     plt.show()
 
 **Total running time of the script:**
-(0 minutes 1.487 seconds)
+(0 minutes 1.283 seconds)
 
 
 
 
@@ -115,7 +115,7 @@ Plot result
 
 
 **Total running time of the script:**
-(0 minutes 0.652 seconds)
+(0 minutes 0.584 seconds)
 
 
 
 
@@ -111,7 +111,7 @@ percolation instability.
     plt.show()
 
 **Total running time of the script:**
-(0 minutes 3.234 seconds)
+(0 minutes 2.902 seconds)
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`@@ -335,7 +335,7 @@ Visualization`
`335`	`335`
`336`	`336`
`337`	`337`	`Total running time of the script:`
`338`		`-(0 minutes 11.838 seconds)`
	`338`	`+(0 minutes 10.473 seconds)`
`339`	`339`
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`@@ -158,7 +158,7 @@ Plot the data and the predicted probabilities`
`158`	`158`
`159`	`159`
`160`	`160`	`Total running time of the script:`
`161`		`-(0 minutes 0.286 seconds)`
	`161`	`+(0 minutes 0.263 seconds)`
`162`	`162`
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`@@ -146,7 +146,7 @@ Plot calibration plots`
`146`	`146`
`147`	`147`
`148`	`148`	`Total running time of the script:`
`149`		`-(0 minutes 2.544 seconds)`
	`149`	`+(0 minutes 2.462 seconds)`
`150`	`150`
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`@@ -197,7 +197,7 @@ plot functions`
`197`	`197`
`198`	`198`
`199`	`199`	`Total running time of the script:`
`200`		`-(0 minutes 0.447 seconds)`
	`200`	`+(0 minutes 0.393 seconds)`
`201`	`201`
`202`	`202`
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`@@ -115,7 +115,7 @@ Plot result`
`115`	`115`
`116`	`116`
`117`	`117`	`Total running time of the script:`
`118`		`-(0 minutes 0.652 seconds)`
	`118`	`+(0 minutes 0.584 seconds)`
`119`	`119`
`120`	`120`
`121`	`121`