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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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diff --git a/‎dev/_sources/auto_examples/bicluster/plot_spectral_biclustering.txt
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diff --git a/‎dev/_sources/auto_examples/classification/plot_classification_probability.txt
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diff --git a/‎dev/_sources/auto_examples/classification/plot_digits_classification.txt
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diff --git a/‎dev/_sources/auto_examples/classification/plot_lda.txt
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@@ -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: 0.025524s
+    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.026809s
 
     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: 0.018957s
+    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.020602s
 
     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: 0.015846s
+    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.016319s
 
     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: 0.014189s
+    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.013587s
 
     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: 0.000367s
+    Complexity: 69 | MSE: 31.8133 | Pred. Time: 0.000394s
 
     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: 0.000676s
+    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000694s
 
     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: 0.001105s
+    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001188s
 
     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: 0.001560s
+    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001675s
 
     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: 0.001794s
+    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001924s
 
     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: 0.000195s
+    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000212s
 
     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: 0.000299s
 
     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: 0.000446s
+    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: 0.000978s
+    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.001046s
 
 
-**Total running time of the script:** ( 0 minutes  23.230 seconds)
+**Total running time of the script:** ( 0 minutes  24.009 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 1.60s (  600 docs/s)
-              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.61s (  598 docs/s)
-                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.61s (  596 docs/s)
-          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.65s (  584 docs/s)
+      Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.913 in 1.72s (  560 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.72s (  558 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.73s (  557 docs/s)
+          NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.76s (  545 docs/s)
 
 
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 4.74s (  824 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 4.75s (  824 docs/s)
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 4.75s (  823 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 4.78s (  817 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 5.01s (  780 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.01s (  780 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 5.02s (  779 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.05s (  773 docs/s)
 
 
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 7.76s (  878 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 7.77s (  878 docs/s)
-                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 7.77s (  877 docs/s)
-          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 7.80s (  874 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 8.32s (  820 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 8.32s (  819 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 8.32s (  819 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 8.36s (  815 docs/s)
 
 
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 10.86s (  899 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 10.86s (  898 docs/s)
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 10.86s (  898 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 10.90s (  895 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 11.59s (  842 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 11.59s (  841 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 11.60s (  841 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 11.63s (  838 docs/s)
 
 
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.61s (  858 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.61s (  858 docs/s)
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 13.62s (  857 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 13.65s (  855 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.54s (  803 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.54s (  803 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.55s (  802 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 14.58s (  800 docs/s)
 
 
-      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 16.81s (  869 docs/s)
-              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 16.82s (  869 docs/s)
-                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 16.82s (  869 docs/s)
-          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 16.85s (  867 docs/s)
+      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 17.88s (  818 docs/s)
+              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 17.88s (  817 docs/s)
+                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 17.88s (  817 docs/s)
+          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 17.92s (  816 docs/s)
 
 
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 19.79s (  877 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 19.79s (  877 docs/s)
-                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 19.80s (  876 docs/s)
-          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 19.83s (  875 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 20.99s (  827 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 20.99s (  826 docs/s)
+                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 21.00s (  826 docs/s)
+          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 21.03s (  825 docs/s)
 
 
 Plot results
@@ -526,7 +526,7 @@ Plot results
 
 
 
-**Total running time of the script:** ( 0 minutes  21.687 seconds)
+**Total running time of the script:** ( 0 minutes  22.986 seconds)
 
 
 
 
@@ -158,7 +158,7 @@ risk of over-fitting the data.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  4.375 seconds)
+**Total running time of the script:** ( 0 minutes  4.634 seconds)
 
 
 
 
@@ -372,10 +372,10 @@ main code
     benchmarking with 100 features
     benchmarking with 250 features
     benchmarking with 500 features
-    example run in 3.55s
+    example run in 3.76s
 
 
-**Total running time of the script:** ( 0 minutes  3.556 seconds)
+**Total running time of the script:** ( 0 minutes  3.764 seconds)
 
 
 
 
@@ -65,7 +65,7 @@ References
 
      Area under the ROC curve : 0.993919
 
-    time elapsed: 6.36s
+    time elapsed: 6.76s
 
 
 
@@ -247,7 +247,7 @@ References
     plot_species_distribution()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  6.357 seconds)
+**Total running time of the script:** ( 0 minutes  6.762 seconds)
 
 
 
 
@@ -340,7 +340,7 @@ Visualization
 
 
 
-**Total running time of the script:** ( 0 minutes  16.424 seconds)
+**Total running time of the script:** ( 0 minutes  15.169 seconds)
 
 
 
 
@@ -164,7 +164,7 @@ contributed to fewer projections than the central disk.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  9.216 seconds)
+**Total running time of the script:** ( 0 minutes  9.866 seconds)
 
 
 
 
@@ -106,7 +106,7 @@ representation of the checkerboard structure.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.898 seconds)
+**Total running time of the script:** ( 0 minutes  0.926 seconds)
 
 
 
 
@@ -93,7 +93,7 @@ the biclusters.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.222 seconds)
+**Total running time of the script:** ( 0 minutes  0.233 seconds)
 
 
 
 
@@ -159,7 +159,7 @@ Plot the data and the predicted probabilities
 
 
 
-**Total running time of the script:** ( 0 minutes  0.262 seconds)
+**Total running time of the script:** ( 0 minutes  0.277 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  2.127 seconds)
+**Total running time of the script:** ( 0 minutes  2.232 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  0.656 seconds)
+**Total running time of the script:** ( 0 minutes  0.701 seconds)
 
 
 
 
@@ -147,7 +147,7 @@ Plot calibration plots
 
 
 
-**Total running time of the script:** ( 0 minutes  2.481 seconds)
+**Total running time of the script:** ( 0 minutes  2.695 seconds)
 
 
 
 
@@ -113,7 +113,7 @@ a result it can identify only the first class.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  2.384 seconds)
+**Total running time of the script:** ( 0 minutes  2.543 seconds)
 
 
 
 
@@ -153,7 +153,7 @@ set.
     plt.tight_layout()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  7.554 seconds)
+**Total running time of the script:** ( 0 minutes  7.914 seconds)
 
 
 
 
@@ -120,7 +120,7 @@ This example is commented in the
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.731 seconds)
+**Total running time of the script:** ( 0 minutes  0.762 seconds)
 
 
 
 
@@ -86,7 +86,7 @@ Shows how shrinkage improves classification.
     shrinkage Linear Discriminant Analysis (1 discriminative feature)')
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  5.695 seconds)
+**Total running time of the script:** ( 0 minutes  6.104 seconds)
 
 
 
 
@@ -204,7 +204,7 @@ plot functions
 
 
 
-**Total running time of the script:** ( 0 minutes  0.390 seconds)
+**Total running time of the script:** ( 0 minutes  0.419 seconds)
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`@@ -158,7 +158,7 @@ risk of over-fitting the data.`
`158`	`158`
`159`	`159`	`plt.show()`
`160`	`160`
`161`		`-Total running time of the script: ( 0 minutes 4.375 seconds)`
	`161`	`+Total running time of the script: ( 0 minutes 4.634 seconds)`
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`@@ -340,7 +340,7 @@ Visualization`
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`341`	`341`
`342`	`342`
`343`		`-Total running time of the script: ( 0 minutes 16.424 seconds)`
	`343`	`+Total running time of the script: ( 0 minutes 15.169 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 9.216 seconds)`
	`167`	`+Total running time of the script: ( 0 minutes 9.866 seconds)`
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`@@ -106,7 +106,7 @@ representation of the checkerboard structure.`
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`107`	`107`	`plt.show()`
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`109`		`-Total running time of the script: ( 0 minutes 0.898 seconds)`
	`109`	`+Total running time of the script: ( 0 minutes 0.926 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 0.222 seconds)`
	`96`	`+Total running time of the script: ( 0 minutes 0.233 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 0.262 seconds)`
	`162`	`+Total running time of the script: ( 0 minutes 0.277 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 2.127 seconds)`
	`217`	`+Total running time of the script: ( 0 minutes 2.232 seconds)`
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`@@ -204,7 +204,7 @@ would have resulted in a similar decrease in log-loss.`
`204`	`204`
`205`	`205`	`plt.show()`
`206`	`206`
`207`		`-Total running time of the script: ( 0 minutes 0.656 seconds)`
	`207`	`+Total running time of the script: ( 0 minutes 0.701 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 2.481 seconds)`
	`150`	`+Total running time of the script: ( 0 minutes 2.695 seconds)`
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`@@ -113,7 +113,7 @@ a result it can identify only the first class.`
`113`	`113`
`114`	`114`	`plt.show()`
`115`	`115`
`116`		`-Total running time of the script: ( 0 minutes 2.384 seconds)`
	`116`	`+Total running time of the script: ( 0 minutes 2.543 seconds)`
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`119`	`119`