Pushing the docs to dev/ for branch: master, commit d2f8292e11af5e1e019a982fa871b7b254150f95

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -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: 0.029489s
+    Complexity: 4454 | Hamming Loss (Misclassification Ratio): 0.2501 | Pred. Time: 0.028000s
 
     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: 0.021876s
+    Complexity: 1624 | Hamming Loss (Misclassification Ratio): 0.2923 | Pred. Time: 0.021235s
 
     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: 0.016780s
+    Complexity: 873 | Hamming Loss (Misclassification Ratio): 0.3191 | Pred. Time: 0.017545s
 
     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: 0.014468s
+    Complexity: 655 | Hamming Loss (Misclassification Ratio): 0.3252 | Pred. Time: 0.015625s
 
     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: 0.000389s
+    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: 0.000702s
+    Complexity: 136 | MSE: 25.6140 | Pred. Time: 0.000693s
 
     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: 0.001198s
+    Complexity: 243 | MSE: 22.3315 | Pred. Time: 0.001359s
 
     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: 0.001700s
+    Complexity: 350 | MSE: 21.3679 | Pred. Time: 0.001671s
 
     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: 0.001934s
+    Complexity: 404 | MSE: 21.0915 | Pred. Time: 0.001907s
 
     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: 0.000120s
+    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: 0.000212s
+    Complexity: 50 | MSE: 8.3398 | Pred. Time: 0.000208s
 
     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: 0.000306s
+    Complexity: 100 | MSE: 7.0096 | Pred. Time: 0.000313s
 
     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: 0.000484s
+    Complexity: 200 | MSE: 6.1836 | Pred. Time: 0.000476s
 
     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: 0.001058s
+    Complexity: 500 | MSE: 6.3426 | Pred. Time: 0.001060s
 
 
-**Total running time of the script:** ( 0 minutes  25.226 seconds)
+**Total running time of the script:** ( 0 minutes  25.285 seconds)
 
 
 

dev/_downloads/auto_examples_python.zip

@@ -357,45 +357,45 @@ maximum
 
     Test set is 878 documents (108 positive)
       Passive-Aggressive classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.913 in 1.77s (  544 docs/s)
-              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.77s (  543 docs/s)
-                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.77s (  542 docs/s)
+              Perceptron classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.921 in 1.77s (  542 docs/s)
+                     SGD classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.925 in 1.78s (  541 docs/s)
           NB Multinomial classifier :          962 train docs (   132 positive)    878 test docs (   108 positive) accuracy: 0.877 in 1.81s (  530 docs/s)
 
 
-      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 5.15s (  759 docs/s)
-              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.15s (  759 docs/s)
-                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 5.16s (  758 docs/s)
-          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.19s (  753 docs/s)
+      Passive-Aggressive classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.946 in 5.16s (  758 docs/s)
+              Perceptron classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.926 in 5.16s (  758 docs/s)
+                     SGD classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.945 in 5.16s (  757 docs/s)
+          NB Multinomial classifier :         3911 train docs (   517 positive)    878 test docs (   108 positive) accuracy: 0.885 in 5.20s (  751 docs/s)
 
 
-      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 8.52s (  801 docs/s)
-              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 8.52s (  800 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 8.56s (  796 docs/s)
+      Passive-Aggressive classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.951 in 8.52s (  800 docs/s)
+              Perceptron classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.949 in 8.53s (  799 docs/s)
+                     SGD classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.938 in 8.53s (  799 docs/s)
+          NB Multinomial classifier :         6821 train docs (   891 positive)    878 test docs (   108 positive) accuracy: 0.899 in 8.57s (  796 docs/s)
 
 
-      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 11.89s (  820 docs/s)
-              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 11.89s (  820 docs/s)
-                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 11.90s (  820 docs/s)
-          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 11.93s (  817 docs/s)
+      Passive-Aggressive classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.964 in 11.86s (  822 docs/s)
+              Perceptron classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.950 in 11.87s (  822 docs/s)
+                     SGD classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.958 in 11.87s (  821 docs/s)
+          NB Multinomial classifier :         9759 train docs (  1276 positive)    878 test docs (   108 positive) accuracy: 0.909 in 11.91s (  819 docs/s)
 
 
-      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.96s (  780 docs/s)
-              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.96s (  780 docs/s)
-                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.96s (  780 docs/s)
-          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 15.00s (  778 docs/s)
+      Passive-Aggressive classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.88s (  784 docs/s)
+              Perceptron classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.88s (  784 docs/s)
+                     SGD classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.951 in 14.89s (  784 docs/s)
+          NB Multinomial classifier :        11680 train docs (  1499 positive)    878 test docs (   108 positive) accuracy: 0.916 in 14.92s (  782 docs/s)
 
 
-      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 18.37s (  796 docs/s)
-              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 18.37s (  796 docs/s)
-                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 18.38s (  795 docs/s)
-          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 18.41s (  794 docs/s)
+      Passive-Aggressive classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.966 in 18.34s (  797 docs/s)
+              Perceptron classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.956 in 18.35s (  797 docs/s)
+                     SGD classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.954 in 18.35s (  796 docs/s)
+          NB Multinomial classifier :        14625 train docs (  1865 positive)    878 test docs (   108 positive) accuracy: 0.926 in 18.39s (  795 docs/s)
 
 
-      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 21.53s (  806 docs/s)
-              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 21.54s (  806 docs/s)
-                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 21.54s (  805 docs/s)
-          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 21.58s (  804 docs/s)
+      Passive-Aggressive classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.954 in 21.52s (  806 docs/s)
+              Perceptron classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.957 in 21.52s (  806 docs/s)
+                     SGD classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.949 in 21.53s (  806 docs/s)
+          NB Multinomial classifier :        17360 train docs (  2179 positive)    878 test docs (   108 positive) accuracy: 0.932 in 21.56s (  805 docs/s)
 
 
 Plot results
@@ -526,7 +526,7 @@ Plot results
 
 
 
-**Total running time of the script:** ( 0 minutes  23.561 seconds)
+**Total running time of the script:** ( 0 minutes  23.547 seconds)
 
 
 

dev/_downloads/scikit-learn-docs.pdf

@@ -158,7 +158,7 @@ risk of over-fitting the data.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  4.720 seconds)
+**Total running time of the script:** ( 0 minutes  4.769 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_001.png

@@ -372,10 +372,10 @@ main code
     benchmarking with 100 features
     benchmarking with 250 features
     benchmarking with 500 features
-    example run in 3.85s
+    example run in 3.91s
 
 
-**Total running time of the script:** ( 0 minutes  3.854 seconds)
+**Total running time of the script:** ( 0 minutes  3.910 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0011.png

@@ -65,7 +65,7 @@ References
 
      Area under the ROC curve : 0.993919
 
-    time elapsed: 6.82s
+    time elapsed: 6.84s
 
 
 
@@ -247,7 +247,7 @@ References
     plot_species_distribution()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  6.826 seconds)
+**Total running time of the script:** ( 0 minutes  6.843 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_002.png

@@ -340,7 +340,7 @@ Visualization
 
 
 
-**Total running time of the script:** ( 0 minutes  11.446 seconds)
+**Total running time of the script:** ( 0 minutes  11.194 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0021.png

@@ -164,7 +164,7 @@ contributed to fewer projections than the central disk.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  9.916 seconds)
+**Total running time of the script:** ( 0 minutes  9.900 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_003.png

@@ -106,7 +106,7 @@ representation of the checkerboard structure.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.909 seconds)
+**Total running time of the script:** ( 0 minutes  0.938 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0031.png

@@ -93,7 +93,7 @@ the biclusters.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.240 seconds)
+**Total running time of the script:** ( 0 minutes  0.241 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_004.png

@@ -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  2.261 seconds)
+**Total running time of the script:** ( 0 minutes  2.280 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_0041.png

@@ -204,7 +204,7 @@ would have resulted in a similar decrease in log-loss.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.703 seconds)
+**Total running time of the script:** ( 0 minutes  0.705 seconds)
 
 
 

dev/_images/sphx_glr_plot_agglomerative_clustering_thumb.png

@@ -147,7 +147,7 @@ Plot calibration plots
 
 
 
-**Total running time of the script:** ( 0 minutes  2.659 seconds)
+**Total running time of the script:** ( 0 minutes  2.732 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_001.png

@@ -113,7 +113,7 @@ a result it can identify only the first class.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  2.565 seconds)
+**Total running time of the script:** ( 0 minutes  2.588 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0011.png

@@ -153,7 +153,7 @@ set.
     plt.tight_layout()
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  8.043 seconds)
+**Total running time of the script:** ( 0 minutes  8.075 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_002.png

@@ -120,7 +120,7 @@ This example is commented in the
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  0.797 seconds)
+**Total running time of the script:** ( 0 minutes  0.788 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_0021.png

@@ -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  6.176 seconds)
+**Total running time of the script:** ( 0 minutes  6.236 seconds)
 
 
 

dev/_images/sphx_glr_plot_approximate_nearest_neighbors_scalability_thumb.png

@@ -204,7 +204,7 @@ plot functions
 
 
 
-**Total running time of the script:** ( 0 minutes  0.425 seconds)
+**Total running time of the script:** ( 0 minutes  0.426 seconds)
 
 
 

dev/_images/sphx_glr_plot_cluster_comparison_001.png

@@ -50,17 +50,17 @@ value of k on various overlapping sub-samples of the dataset.
     Computing v_measure_score for 10 values of n_clusters and n_samples=100
     done in 0.064s
     Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=100
-    done in 0.486s
+    done in 0.488s
     Computing mutual_info_score for 10 values of n_clusters and n_samples=100
     done in 0.054s
     Computing adjusted_rand_score for 10 values of n_clusters and n_samples=1000
-    done in 0.066s
+    done in 0.071s
     Computing v_measure_score for 10 values of n_clusters and n_samples=1000
-    done in 0.085s
+    done in 0.088s
     Computing adjusted_mutual_info_score for 10 values of n_clusters and n_samples=1000
-    done in 0.308s
+    done in 0.320s
     Computing mutual_info_score for 10 values of n_clusters and n_samples=1000
-    done in 0.069s
+    done in 0.074s
 
 
 
@@ -171,7 +171,7 @@ value of k on various overlapping sub-samples of the dataset.
     plt.legend(plots, names)
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  1.416 seconds)
+**Total running time of the script:** ( 0 minutes  1.449 seconds)
 
 
 

dev/_images/sphx_glr_plot_cluster_comparison_0011.png

@@ -118,7 +118,7 @@ Plot result
 
 
 
-**Total running time of the script:** ( 0 minutes  0.718 seconds)
+**Total running time of the script:** ( 0 minutes  0.739 seconds)
 
 
 

dev/_images/sphx_glr_plot_cluster_comparison_thumb.png

@@ -111,7 +111,7 @@ percolation instability.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  2.902 seconds)
+**Total running time of the script:** ( 0 minutes  3.154 seconds)
 
 
 

dev/_images/sphx_glr_plot_compare_methods_001.png

@@ -179,7 +179,7 @@ thus the clustering puts them in the same cluster.
 
     plt.show()
 
-**Total running time of the script:** ( 0 minutes  1.295 seconds)
+**Total running time of the script:** ( 0 minutes  1.351 seconds)