Objective Revision Evaluation Service/reverted
One of the most critical concerns about Wikimedia's open projects is the detection and removal of damaging contributions. This model predicts whether or not an edit will be likely to need to be reverted. It is useful for quality control tools (e.g. en:WP:Huggle and en:User:ClueBot NG)
This model is trained to predict 'reverted' edits. Not all reverted edits are "vandalism". Consume scores with this in mind.
Contexts (wikis)
editArabic Wikipedia (arwiki)
edithttps://ores.wmflabs.org/v2/scores/arwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: min_samples_leaf=1, max_features="log2", n_estimators=700, learning_rate=0.01, presort="auto", verbose=0, min_weight_fraction_leaf=0.0, balanced_sample_weight=true, balanced_sample=false, center=true, max_leaf_nodes=null, max_depth=5, subsample=1.0, random_state=null, loss="deviance", init=null, warm_start=false, min_samples_split=2, scale=true
- version: 0.3.0
- trained: 2017-01-06T19:06:15.589011
Table:
~False ~True
----- -------- -------
False 17964 1027
True 72 615
Accuracy: 0.944
Precision:
----- -----
False 0.996
True 0.375
----- -----
Recall:
----- -----
False 0.946
True 0.896
----- -----
PR-AUC:
----- -----
False 0.994
True 0.514
----- -----
ROC-AUC:
----- -----
False 0.963
True 0.967
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.602 0.928 0.093
True 0.229 0.926 0.088
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.068 0.984 0.98
True 0.97 0.057 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.028 1 0.966
True 0.97 0.057 0.987
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.028 1 0.966
True 0.827 0.697 0.455
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.028 1 0.966
True 0.114 0.951 0.177
Czech Wikipedia (cswiki)
edithttps://ores.wmflabs.org/v2/scores/cswiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: learning_rate=0.01, presort="auto", subsample=1.0, random_state=null, n_estimators=700, min_samples_leaf=1, verbose=0, max_depth=7, balanced_sample_weight=true, min_weight_fraction_leaf=0.0, loss="deviance", warm_start=false, init=null, min_samples_split=2, center=true, balanced_sample=false, max_leaf_nodes=null, scale=true, max_features="log2"
- version: 0.3.0
- trained: 2017-01-06T19:12:50.748800
Table:
~False ~True
----- -------- -------
False 18141 1129
True 180 395
Accuracy: 0.934
Precision:
----- -----
False 0.99
True 0.259
----- -----
Recall:
----- -----
False 0.941
True 0.685
----- -----
PR-AUC:
----- -----
False 0.994
True 0.376
----- -----
ROC-AUC:
----- -----
False 0.919
True 0.92
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.904 0.746 0.094
True 0.272 0.773 0.096
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.147 0.987 0.98
True 0.957 0.065 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.038 1 0.972
True 0.957 0.065 1
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.038 1 0.972
True 0.86 0.32 0.467
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.038 1 0.972
True 0.191 0.824 0.159
German Wikipedia (dewiki)
edithttps://ores.wmflabs.org/v2/scores/dewiki/reverted?model_info
- type: GradientBoosting
- params: min_weight_fraction_leaf=0.0, center=true, min_samples_split=2, balanced_sample=false, min_samples_leaf=1, subsample=1.0, scale=true, n_estimators=300, presort="auto", max_leaf_nodes=null, random_state=null, init=null, warm_start=false, max_depth=3, balanced_sample_weight=true, max_features="log2", learning_rate=0.1, verbose=0, loss="deviance"
- version: 0.3.0
- trained: 2017-01-06T19:17:16.259241
Table:
~False ~True
----- -------- -------
False 16847 1983
True 254 729
Accuracy: 0.887
Precision:
----- -----
False 0.985
True 0.269
----- -----
Recall:
----- -----
False 0.895
True 0.741
----- -----
PR-AUC:
----- -----
False 0.99
True 0.451
----- -----
ROC-AUC:
----- -----
False 0.889
True 0.888
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.835 0.586 0.096
True 0.54 0.733 0.097
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.282 0.933 0.98
True 0.967 0.075 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.023 1 0.952
True 0.956 0.113 0.951
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.023 1 0.952
True 0.876 0.425 0.471
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.023 1 0.952
True 0.258 0.848 0.156
English Wikipedia (enwiki)
edithttps://ores.wmflabs.org/v2/scores/enwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: min_samples_split=2, max_depth=7, balanced_sample_weight=true, warm_start=false, presort="auto", scale=true, learning_rate=0.01, random_state=null, max_features="log2", balanced_sample=false, init=null, verbose=0, center=true, loss="deviance", min_samples_leaf=1, n_estimators=700, min_weight_fraction_leaf=0.0, max_leaf_nodes=null, subsample=1.0
- version: 0.3.0
- trained: 2017-01-06T19:23:24.945358
Table:
~False ~True
----- -------- -------
False 15554 2560
True 457 962
Accuracy: 0.846
Precision:
----- -----
False 0.971
True 0.273
----- -----
Recall:
----- -----
False 0.859
True 0.68
----- -----
PR-AUC:
----- -----
False 0.984
True 0.424
----- -----
ROC-AUC:
----- -----
False 0.866
True 0.867
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.852 0.597 0.096
True 0.605 0.583 0.098
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.688 0.768 0.98
True 0.94 0.039 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.054 1 0.929
True 0.926 0.071 0.947
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.054 1 0.929
True 0.758 0.378 0.455
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.054 1 0.929
True 0.157 0.898 0.155
English Wiktionary (enwiktionary)
edithttps://ores.wmflabs.org/v2/scores/enwiktionary/reverted?model_info
ScikitLearnClassifier
- type: RF
- params: oob_score=false, verbose=0, min_samples_split=2, min_samples_leaf=3, class_weight=null, center=true, n_estimators=320, max_depth=null, max_leaf_nodes=null, warm_start=false, balanced_sample=false, max_features="log2", n_jobs=1, balanced_sample_weight=true, random_state=null, criterion="entropy", bootstrap=true, min_weight_fraction_leaf=0.0, scale=true
- version: 0.3.0
- trained: 2017-01-06T19:44:30.000302
Table:
~False ~True
----- -------- -------
False 19808 183
True 279 574
Accuracy: 0.978
Precision:
----- -----
False 0.986
True 0.76
----- -----
Recall:
----- -----
False 0.991
True 0.675
----- -----
PR-AUC:
----- -----
False 0.995
True 0.742
----- -----
ROC-AUC:
----- -----
False 0.972
True 0.974
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.863 0.915 0.094
True 0.124 0.919 0.095
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.267 0.996 0.981
True 0.944 0.126 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.038 1 0.961
True 0.873 0.321 0.92
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.038 1 0.961
True 0.2 0.832 0.459
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.038 1 0.961
True 0.044 0.984 0.16
Spanish Wikipedia (eswiki)
edithttps://ores.wmflabs.org/v2/scores/eswiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: min_weight_fraction_leaf=0.0, max_leaf_nodes=null, verbose=0, init=null, subsample=1.0, presort="auto", random_state=null, balanced_sample=false, loss="deviance", scale=true, learning_rate=0.01, min_samples_split=2, max_features="log2", warm_start=false, balanced_sample_weight=true, n_estimators=700, center=true, min_samples_leaf=1, max_depth=7
- version: 0.3.0
- trained: 2017-01-06T19:51:18.041685
Table:
~False ~True
----- -------- -------
False 14751 2881
True 485 1697
Accuracy: 0.83
Precision:
----- -----
False 0.968
True 0.37
----- -----
Recall:
----- -----
False 0.837
True 0.778
----- -----
PR-AUC:
----- -----
False 0.983
True 0.584
----- -----
ROC-AUC:
----- -----
False 0.901
True 0.901
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.75 0.726 0.099
True 0.643 0.658 0.098
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.686 0.757 0.98
True 0.957 0.047 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.067 0.997 0.901
True 0.937 0.1 0.919
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.035 1 0.892
True 0.643 0.657 0.453
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.035 1 0.892
True 0.053 0.983 0.154
Spanish Wikibooks (eswikibooks)
edithttps://ores.wmflabs.org/v2/scores/eswikibooks/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: subsample=1.0, balanced_sample_weight=true, init=null, min_samples_leaf=1, max_leaf_nodes=null, learning_rate=0.01, n_estimators=700, random_state=null, loss="deviance", center=true, verbose=0, warm_start=false, min_weight_fraction_leaf=0.0, presort="auto", max_depth=7, scale=true, balanced_sample=false, min_samples_split=2, max_features="log2"
- version: 0.3.0
- trained: 2017-01-06T19:57:03.343720
Table:
~False ~True
----- -------- -------
False 16173 1164
True 129 1573
Accuracy: 0.932
Precision:
----- -----
False 0.992
True 0.574
----- -----
Recall:
----- -----
False 0.933
True 0.924
----- -----
PR-AUC:
----- -----
False 0.994
True 0.818
----- -----
ROC-AUC:
----- -----
False 0.976
True 0.978
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.401 0.943 0.095
True 0.166 0.96 0.096
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.196 0.97 0.981
True 0.98 0.109 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.016 1 0.915
True 0.96 0.359 0.908
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.016 1 0.915
True 0.112 0.97 0.466
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.016 1 0.915
True 0.015 0.996 0.178
Estonian Wikipedia (etwiki)
edithttps://ores.wmflabs.org/v2/scores/etwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: presort="auto", max_depth=7, min_samples_split=2, balanced_sample_weight=true, learning_rate=0.01, init=null, balanced_sample=false, verbose=0, min_samples_leaf=1, scale=true, max_leaf_nodes=null, subsample=1.0, loss="deviance", max_features="log2", n_estimators=500, warm_start=false, random_state=null, min_weight_fraction_leaf=0.0, center=true
- version: 0.3.0
- trained: 2017-01-06T20:02:25.031504
Table:
~False ~True
----- -------- -------
False 18666 810
True 106 288
Accuracy: 0.954
Precision:
----- -----
False 0.994
True 0.263
----- -----
Recall:
----- -----
False 0.958
True 0.728
----- -----
PR-AUC:
----- -----
False 0.995
True 0.532
----- -----
ROC-AUC:
----- -----
False 0.943
True 0.942
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.858 0.834 0.089
True 0.18 0.877 0.093
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.033 1 0.983
True 0.959 0.211 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.025 1 0.982
True 0.952 0.24 0.945
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.025 1 0.982
True 0.819 0.49 0.478
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.025 1 0.982
True 0.215 0.865 0.161
Persian Wikipedia (fawiki)
edithttps://ores.wmflabs.org/v2/scores/fawiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: presort="auto", center=true, scale=true, subsample=1.0, max_features="log2", balanced_sample_weight=true, min_weight_fraction_leaf=0.0, random_state=null, min_samples_leaf=1, verbose=0, warm_start=false, learning_rate=0.01, max_depth=7, balanced_sample=false, max_leaf_nodes=null, min_samples_split=2, loss="deviance", n_estimators=700, init=null
- version: 0.3.0
- trained: 2017-01-06T20:09:09.014465
Table:
~False ~True
----- -------- -------
False 18405 935
True 167 297
Accuracy: 0.944
Precision:
----- -----
False 0.991
True 0.244
----- -----
Recall:
----- -----
False 0.952
True 0.646
----- -----
PR-AUC:
----- -----
False 0.994
True 0.319
----- -----
ROC-AUC:
----- -----
False 0.933
True 0.938
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.875 0.808 0.09
True 0.254 0.814 0.097
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.093 0.995 0.98
True 0.96 0.041 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.041 1 0.977
True 0.96 0.041 1
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.041 1 0.977
True 0.895 0.207 0.483
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.041 1 0.977
True 0.233 0.837 0.157
French Wikipedia (frwiki)
edithttps://ores.wmflabs.org/v2/scores/frwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: verbose=0, max_leaf_nodes=null, warm_start=false, scale=true, random_state=null, max_features="log2", presort="auto", min_samples_split=2, learning_rate=0.01, center=true, min_samples_leaf=1, max_depth=5, balanced_sample_weight=true, n_estimators=700, init=null, subsample=1.0, balanced_sample=false, loss="deviance", min_weight_fraction_leaf=0.0
- version: 0.3.0
- trained: 2017-01-06T20:26:23.424804
Table:
~False ~True
----- -------- -------
False 17260 1951
True 158 538
Accuracy: 0.894
Precision:
----- -----
False 0.991
True 0.216
----- -----
Recall:
----- -----
False 0.898
True 0.772
----- -----
PR-AUC:
----- -----
False 0.994
True 0.438
----- -----
ROC-AUC:
----- -----
False 0.914
True 0.914
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.799 0.733 0.09
True 0.533 0.779 0.096
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.151 0.976 0.98
True 0.954 0.086 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.04 1 0.966
True 0.95 0.102 0.976
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.04 1 0.966
True 0.866 0.424 0.461
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.04 1 0.966
True 0.273 0.858 0.158
Hebrew Wikipedia (hewiki)
edithttps://ores.wmflabs.org/v2/scores/hewiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: balanced_sample_weight=true, balanced_sample=false, max_depth=7, warm_start=false, loss="deviance", subsample=1.0, max_features="log2", max_leaf_nodes=null, min_samples_split=2, learning_rate=0.01, verbose=0, init=null, random_state=null, min_weight_fraction_leaf=0.0, center=true, scale=true, n_estimators=500, min_samples_leaf=1, presort="auto"
- version: 0.3.0
- trained: 2017-01-06T20:31:51.924465
Table:
~False ~True
----- -------- -------
False 17245 1689
True 275 685
Accuracy: 0.901
Precision:
----- -----
False 0.984
True 0.289
----- -----
Recall:
----- -----
False 0.911
True 0.714
----- -----
PR-AUC:
----- -----
False 0.991
True 0.407
----- -----
ROC-AUC:
----- -----
False 0.899
True 0.901
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.818 0.708 0.095
True 0.45 0.745 0.098
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.358 0.935 0.98
True 0.942 0.046 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.053 1 0.953
True 0.939 0.059 0.975
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.053 1 0.953
True 0.855 0.34 0.467
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.053 1 0.953
True 0.2 0.88 0.155
Hungarian Wikipedia (huwiki)
edithttps://ores.wmflabs.org/v2/scores/huwiki/reverted?model_info
ScikitLearnClassifier
- type: RF
- params: verbose=0, bootstrap=true, min_weight_fraction_leaf=0.0, max_leaf_nodes=null, warm_start=false, min_samples_leaf=13, oob_score=false, n_estimators=320, n_jobs=1, min_samples_split=2, balanced_sample_weight=true, class_weight=null, scale=true, criterion="entropy", max_features="log2", max_depth=null, balanced_sample=false, random_state=null, center=true
- version: 0.3.0
- trained: 2017-01-06T20:53:22.021344
Table:
~False ~True
----- -------- -------
False 38248 990
True 218 372
Accuracy: 0.97
Precision:
----- -----
False 0.994
True 0.274
----- -----
Recall:
----- -----
False 0.975
True 0.627
----- -----
PR-AUC:
----- -----
False 0.995
True 0.37
----- -----
ROC-AUC:
----- -----
False 0.929
True 0.929
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.912 0.761 0.095
True 0.147 0.8 0.093
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.067 1 0.986
True 0.922 0.074 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.067 1 0.986
True 0.922 0.074 0.992
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.067 1 0.986
True 0.794 0.31 0.467
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.067 1 0.986
True 0.221 0.749 0.169
Indonesian Wikipedia (idwiki)
edithttps://ores.wmflabs.org/v2/scores/idwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: init=null, balanced_sample=false, learning_rate=0.01, scale=true, warm_start=false, subsample=1.0, max_leaf_nodes=null, max_depth=5, random_state=null, balanced_sample_weight=true, presort="auto", min_weight_fraction_leaf=0.0, loss="deviance", verbose=0, max_features="log2", min_samples_leaf=1, min_samples_split=2, n_estimators=700, center=true
- version: 0.3.0
- trained: 2017-01-06T21:32:36.621853
Table:
~False ~True
----- -------- -------
False 85465 12234
True 258 2014
Accuracy: 0.875
Precision:
----- -----
False 0.997
True 0.141
----- -----
Recall:
----- -----
False 0.875
True 0.886
----- -----
PR-AUC:
----- -----
False 0.994
True 0.27
----- -----
ROC-AUC:
----- -----
False 0.94
True 0.945
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.533 0.865 0.098
True 0.616 0.849 0.099
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.076 0.995 0.98
True 0.952 0.011 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.047 1 0.977
True 0.952 0.011 1
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.047 1 0.977
True 0.927 0.105 0.468
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.047 1 0.977
True 0.554 0.869 0.152
Italian Wikipedia (itwiki)
edithttps://ores.wmflabs.org/v2/scores/itwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: center=true, min_weight_fraction_leaf=0.0, balanced_sample=false, learning_rate=0.01, verbose=0, max_leaf_nodes=null, random_state=null, max_depth=7, scale=true, loss="deviance", subsample=1.0, min_samples_split=2, max_features="log2", balanced_sample_weight=true, min_samples_leaf=1, n_estimators=700, init=null, presort="auto", warm_start=false
- version: 0.3.0
- trained: 2017-01-06T21:38:17.924898
Table:
~False ~True
----- -------- -------
False 16471 2397
True 252 639
Accuracy: 0.866
Precision:
----- -----
False 0.985
True 0.211
----- -----
Recall:
----- -----
False 0.873
True 0.717
----- -----
PR-AUC:
----- -----
False 0.992
True 0.334
----- -----
ROC-AUC:
----- -----
False 0.898
True 0.902
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.853 0.724 0.094
True 0.595 0.641 0.098
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.394 0.906 0.98
True 0.936 0.042 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.053 1 0.956
True 0.934 0.046 0.988
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.053 1 0.956
True 0.844 0.21 0.474
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.053 1 0.956
True 0.211 0.874 0.154
Dutch Wikipedia (nlwiki)
edithttps://ores.wmflabs.org/v2/scores/nlwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: subsample=1.0, min_samples_split=2, n_estimators=700, verbose=0, balanced_sample_weight=true, warm_start=false, random_state=null, max_features="log2", init=null, max_leaf_nodes=null, presort="auto", center=true, learning_rate=0.01, min_weight_fraction_leaf=0.0, min_samples_leaf=1, scale=true, max_depth=7, balanced_sample=false, loss="deviance"
- version: 0.3.0
- trained: 2017-01-06T21:44:14.717645
Table:
~False ~True
----- -------- -------
False 16884 1379
True 277 924
Accuracy: 0.915
Precision:
----- -----
False 0.984
True 0.401
----- -----
Recall:
----- -----
False 0.924
True 0.77
----- -----
PR-AUC:
----- -----
False 0.992
True 0.593
----- -----
ROC-AUC:
----- -----
False 0.928
True 0.929
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.865 0.755 0.098
True 0.305 0.831 0.098
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.349 0.942 0.98
True 0.959 0.114 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.032 1 0.942
True 0.943 0.196 0.922
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.032 1 0.942
True 0.673 0.705 0.453
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.032 1 0.942
True 0.101 0.931 0.156
Norwegian Wikipedia (nowiki)
edithttps://ores.wmflabs.org/v2/scores/nowiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: balanced_sample=false, min_weight_fraction_leaf=0.0, min_samples_leaf=1, init=null, max_features="log2", scale=true, random_state=null, presort="auto", max_leaf_nodes=null, warm_start=false, verbose=0, loss="deviance", min_samples_split=2, center=true, balanced_sample_weight=true, n_estimators=500, max_depth=7, subsample=1.0, learning_rate=0.01
- version: 0.3.0
- trained: 2017-01-06T22:08:36.437323
Table:
~False ~True
----- -------- -------
False 38123 1102
True 141 626
Accuracy: 0.969
Precision:
----- -----
False 0.996
True 0.363
----- -----
Recall:
----- -----
False 0.972
True 0.817
----- -----
PR-AUC:
----- -----
False 0.995
True 0.581
----- -----
ROC-AUC:
----- -----
False 0.964
True 0.963
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.807 0.89 0.092
True 0.182 0.902 0.091
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.022 1 0.982
True 0.974 0.116 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.021 1 0.982
True 0.97 0.175 0.923
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.021 1 0.982
True 0.812 0.712 0.455
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.021 1 0.982
True 0.183 0.902 0.165
Polish Wikipedia (plwiki)
edithttps://ores.wmflabs.org/v2/scores/plwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: subsample=1.0, min_samples_leaf=1, max_leaf_nodes=null, init=null, balanced_sample=false, verbose=0, center=true, random_state=null, loss="deviance", presort="auto", n_estimators=700, learning_rate=0.01, min_weight_fraction_leaf=0.0, min_samples_split=2, balanced_sample_weight=true, warm_start=false, max_features="log2", max_depth=5, scale=true
- version: 0.3.0
- trained: 2017-01-06T22:22:13.325027
Table:
~False ~True
----- -------- -------
False 34941 3588
True 276 1155
Accuracy: 0.903
Precision:
----- -----
False 0.992
True 0.244
----- -----
Recall:
----- -----
False 0.907
True 0.807
----- -----
PR-AUC:
----- -----
False 0.994
True 0.42
----- -----
ROC-AUC:
----- -----
False 0.928
True 0.929
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.746 0.794 0.096
True 0.471 0.819 0.098
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.121 0.973 0.98
True 0.954 0.042 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.042 1 0.965
True 0.953 0.054 0.969
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.042 1 0.965
True 0.902 0.366 0.455
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.042 1 0.965
True 0.259 0.898 0.153
Portuguese Wikipedia (ptwiki)
edithttps://ores.wmflabs.org/v2/scores/ptwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: presort="auto", verbose=0, warm_start=false, min_samples_split=2, max_features="log2", random_state=null, max_leaf_nodes=null, loss="deviance", init=null, min_samples_leaf=1, subsample=1.0, center=true, n_estimators=700, min_weight_fraction_leaf=0.0, balanced_sample_weight=true, balanced_sample=false, learning_rate=0.01, scale=true, max_depth=7
- version: 0.3.0
- trained: 2017-01-06T22:36:08.809695
Table:
~False ~True
----- -------- -------
False 14777 3022
True 370 1644
Accuracy: 0.829
Precision:
----- -----
False 0.976
True 0.352
----- -----
Recall:
----- -----
False 0.83
True 0.817
----- -----
PR-AUC:
----- -----
False 0.985
True 0.546
----- -----
ROC-AUC:
----- -----
False 0.905
True 0.907
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.721 0.757 0.097
True 0.673 0.649 0.099
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.59 0.805 0.98
True 0.952 0.035 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.056 0.999 0.903
True 0.926 0.098 0.935
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.036 1 0.899
True 0.701 0.602 0.456
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.036 1 0.899
True 0.057 0.986 0.158
Russian Wikipedia (ruwiki)
edithttps://ores.wmflabs.org/v2/scores/ruwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: balanced_sample=false, warm_start=false, min_weight_fraction_leaf=0.0, presort="auto", center=true, random_state=null, max_depth=5, loss="deviance", verbose=0, subsample=1.0, min_samples_leaf=1, balanced_sample_weight=true, init=null, max_leaf_nodes=null, min_samples_split=2, n_estimators=700, learning_rate=0.01, scale=true, max_features="log2"
- version: 0.3.0
- trained: 2017-01-06T22:52:45.773825
Table:
~False ~True
----- -------- -------
False 15893 2796
True 220 826
Accuracy: 0.847
Precision:
----- -----
False 0.986
True 0.229
----- -----
Recall:
----- -----
False 0.85
True 0.789
----- -----
PR-AUC:
----- -----
False 0.991
True 0.382
----- -----
ROC-AUC:
----- -----
False 0.895
True 0.897
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.767 0.709 0.097
True 0.702 0.664 0.098
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.306 0.904 0.98
True 0.929 0.054 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.062 1 0.949
True 0.922 0.069 0.952
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.062 1 0.949
True 0.855 0.283 0.461
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.062 1 0.949
True 0.232 0.889 0.155
Swedish Wikipedia (svwiki)
edithttps://ores.wmflabs.org/v2/scores/svwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: max_features="log2", warm_start=false, subsample=1.0, max_leaf_nodes=null, random_state=null, min_samples_split=2, n_estimators=500, init=null, max_depth=7, loss="deviance", learning_rate=0.01, scale=true, balanced_sample_weight=true, center=true, min_samples_leaf=1, verbose=0, min_weight_fraction_leaf=0.0, presort="auto", balanced_sample=false
- version: 0.3.0
- trained: 2017-01-06T23:13:40.455191
Table:
~False ~True
----- -------- -------
False 37978 1244
True 124 601
Accuracy: 0.966
Precision:
----- -----
False 0.997
True 0.326
----- -----
Recall:
----- -----
False 0.968
True 0.83
----- -----
PR-AUC:
----- -----
False 0.995
True 0.598
----- -----
ROC-AUC:
----- -----
False 0.969
True 0.971
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.761 0.904 0.093
True 0.231 0.899 0.084
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.025 1 0.984
True 0.971 0.165 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.024 1 0.983
True 0.966 0.199 0.931
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.024 1 0.983
True 0.834 0.691 0.47
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.024 1 0.983
True 0.238 0.899 0.173
Turkish Wikipedia (trwiki)
edithttps://ores.wmflabs.org/v2/scores/trwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: n_estimators=700, warm_start=false, learning_rate=0.01, max_features="log2", random_state=null, max_leaf_nodes=null, presort="auto", min_samples_leaf=1, min_samples_split=2, init=null, min_weight_fraction_leaf=0.0, center=true, scale=true, max_depth=7, balanced_sample=false, subsample=1.0, loss="deviance", verbose=0, balanced_sample_weight=true
- version: 0.3.0
- trained: 2017-01-06T23:19:07.190955
Table:
~False ~True
----- -------- -------
False 14975 2496
True 350 1910
Accuracy: 0.856
Precision:
----- -----
False 0.977
True 0.434
----- -----
Recall:
----- -----
False 0.857
True 0.845
----- -----
PR-AUC:
----- -----
False 0.985
True 0.554
----- -----
ROC-AUC:
----- -----
False 0.916
True 0.919
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.724 0.804 0.098
True 0.712 0.738 0.099
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.586 0.842 0.98
True 0.937 0.02 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.101 0.99 0.901
True 0.934 0.029 0.966
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.061 1 0.887
True 0.591 0.808 0.452
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.061 1 0.887
True 0.044 0.993 0.163
Ukrainian Wikipedia (ukwiki)
edithttps://ores.wmflabs.org/v2/scores/ukwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: max_leaf_nodes=null, presort="auto", learning_rate=0.01, init=null, center=true, random_state=null, max_features="log2", verbose=0, min_samples_leaf=1, scale=true, warm_start=false, min_weight_fraction_leaf=0.0, balanced_sample_weight=true, max_depth=7, balanced_sample=false, min_samples_split=2, loss="deviance", subsample=1.0, n_estimators=700
- version: 0.3.0
- trained: 2017-01-06T23:35:31.227174
Table:
~False ~True
----- -------- -------
False 18519 928
True 210 192
Accuracy: 0.943
Precision:
----- -----
False 0.989
True 0.172
----- -----
Recall:
----- -----
False 0.952
True 0.477
----- -----
PR-AUC:
----- -----
False 0.994
True 0.204
----- -----
ROC-AUC:
----- -----
False 0.852
True 0.853
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.935 0.511 0.087
True 0.303 0.597 0.095
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.095 0.998 0.981
True 0.938 0.073 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.059 1 0.98
True 0.938 0.073 1
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.059 1 0.98
True 0.874 0.132 0.494
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.059 1 0.98
True 0.443 0.525 0.155
Vietnamese Wikipedia (viwiki)
edithttps://ores.wmflabs.org/v2/scores/viwiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: verbose=0, n_estimators=700, scale=true, presort="auto", min_weight_fraction_leaf=0.0, max_leaf_nodes=null, min_samples_split=2, loss="deviance", min_samples_leaf=1, balanced_sample_weight=true, balanced_sample=false, center=true, subsample=1.0, init=null, max_depth=7, warm_start=false, learning_rate=0.01, random_state=null, max_features="log2"
- version: 0.3.0
- trained: 2017-01-07T00:21:37.945283
Table:
~False ~True
----- -------- -------
False 90617 7589
True 336 1458
Accuracy: 0.921
Precision:
----- -----
False 0.996
True 0.161
----- -----
Recall:
----- -----
False 0.923
True 0.813
----- -----
PR-AUC:
----- -----
False 0.995
True 0.457
----- -----
ROC-AUC:
----- -----
False 0.956
True 0.96
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.668 0.876 0.098
True 0.415 0.86 0.098
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.027 1 0.984
True 0.966 0.14 1
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.027 1 0.984
True 0.956 0.188 0.927
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.027 1 0.984
True 0.892 0.415 0.463
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.027 1 0.984
True 0.466 0.834 0.152
Wikidata (wikidatawiki)
edithttps://ores.wmflabs.org/v2/scores/wikidatawiki/reverted?model_info
ScikitLearnClassifier
- type: GradientBoosting
- params: scale=true, balanced_sample=false, max_depth=7, max_features="log2", warm_start=false, min_samples_split=2, init=null, verbose=0, max_leaf_nodes=null, balanced_sample_weight=true, loss="deviance", center=true, subsample=1.0, learning_rate=0.1, random_state=null, presort="auto", min_weight_fraction_leaf=0.0, min_samples_leaf=1, n_estimators=700
- version: 0.3.0
- trained: 2017-01-07T00:46:53.835597
Table:
~False ~True
----- -------- -------
False 11786 1035
True 792 10819
Accuracy: 0.925
Precision:
----- -----
False 0.937
True 0.913
----- -----
Recall:
----- -----
False 0.919
True 0.932
----- -----
PR-AUC:
----- -----
False 0.978
True 0.972
----- -----
ROC-AUC:
----- -----
False 0.977
True 0.979
----- -----
Recall @ 0.1 false-positive rate:
label threshold recall fpr
------- ----------- -------- -----
False 0.334 0.943 0.099
True 0.397 0.948 0.099
Recall @ 0.98 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.886 0.799 0.98
True 0.958 0.664 0.98
Recall @ 0.9 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0.272 0.952 0.901
True 0.422 0.945 0.9
Recall @ 0.45 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0 1 0.537
True 0.001 1 0.516
Recall @ 0.15 precision:
label threshold recall precision
------- ----------- -------- -----------
False 0 1 0.537
True 0.001 1 0.516