| AnotherLadsgroup added a comment. |
Old model without these four features:
ScikitLearnClassifier - type: GradientBoosting - params: learning_rate=0.01, max_features="log2", max_depth=7, loss="deviance", random_state=null, criterion="friedman_mse", balanced_sample=false, presort="auto", min_impurity_split=1e-07, min_weight_fraction_leaf=0.0, scale=true, center=true, verbose=0, max_leaf_nodes=null, subsample=1.0, balanced_sample_weight=true, n_estimators=700, warm_start=false, init=null, min_samples_split=2, min_samples_leaf=1 - version: 0.3.0 - trained: 2017-07-18T18:08:23.587697
Table: ~False ~True ----- -------- ------- False 1222 400 True 201 2442 Accuracy: 0.859 Precision: ----- ----- False 0.856 True 0.859 ----- ----- Recall: ----- ----- False 0.751 True 0.924 ----- ----- PR-AUC: ----- ----- False 0.849 True 0.894 ----- ----- ROC-AUC: ----- ----- False 0.884 True 0.885 ----- ----- Recall @ 0.1 false-positive rate: label threshold recall fpr ------- ----------- -------- ----- False 0.416 0.767 0.096 True 0.814 0.528 0.096 Filter rate @ 0.9 recall: label threshold filter_rate recall ------- ----------- ------------- -------- False 0.187 0.364 0.903 True 0.597 0.354 0.902 Filter rate @ 0.75 recall: label threshold filter_rate recall ------- ----------- ------------- -------- False 0.541 0.658 0.753 True 0.739 0.469 0.751 Recall @ 0.995 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.972 0.083 1 True 0.941 0.043 1 Recall @ 0.99 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.972 0.083 1 True 0.941 0.043 1 Recall @ 0.98 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.972 0.083 1 True 0.941 0.043 1 Recall @ 0.9 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.697 0.667 0.909 True 0.801 0.547 0.903 Recall @ 0.75 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.338 0.79 0.764 True 0.075 0.978 0.759 Recall @ 0.6 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.227 0.858 0.609 True 0.026 1 0.633 Recall @ 0.45 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.147 0.949 0.457 True 0.026 1 0.631 Recall @ 0.15 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.052 1 0.386 True 0.026 1 0.631
With these four features added:
ScikitLearnClassifier - type: GradientBoosting - params: max_features="log2", min_samples_leaf=1, subsample=1.0, scale=true, balanced_sample_weight=true, verbose=0, criterion="friedman_mse", min_samples_split=2, max_leaf_nodes=null, center=true, n_estimators=700, min_weight_fraction_leaf=0.0, max_depth=7, balanced_sample=false, init=null, min_impurity_split=1e-07, warm_start=false, presort="auto", learning_rate=0.01, random_state=null, loss="deviance" - version: 0.3.0 - trained: 2017-07-18T18:14:29.705559 Table: ~False ~True ----- -------- ------- False 1227 395 True 192 2451 Accuracy: 0.862 Precision: ----- ----- False 0.863 True 0.861 ----- ----- Recall: ----- ----- False 0.754 True 0.928 ----- ----- PR-AUC: ----- ----- False 0.857 True 0.898 ----- ----- ROC-AUC: ----- ----- False 0.888 True 0.891 ----- ----- Recall @ 0.1 false-positive rate: label threshold recall fpr ------- ----------- -------- ----- False 0.427 0.773 0.096 True 0.814 0.522 0.097 Filter rate @ 0.9 recall: label threshold filter_rate recall ------- ----------- ------------- -------- False 0.186 0.36 0.903 True 0.59 0.356 0.902 Filter rate @ 0.75 recall: label threshold filter_rate recall ------- ----------- ------------- -------- False 0.552 0.657 0.753 True 0.746 0.475 0.751 Recall @ 0.995 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.971 0.085 1 True 0.939 0.059 1 Recall @ 0.99 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.971 0.085 1 True 0.939 0.059 1 Recall @ 0.98 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.967 0.146 0.995 True 0.939 0.059 1 Recall @ 0.9 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.673 0.669 0.906 True 0.795 0.576 0.903 Recall @ 0.75 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.329 0.807 0.756 True 0.075 0.98 0.76 Recall @ 0.6 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.222 0.869 0.613 True 0.029 0.999 0.638 Recall @ 0.45 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.138 0.955 0.454 True 0.027 1 0.632 Recall @ 0.15 precision: label threshold recall precision ------- ----------- -------- ----------- False 0.05 1 0.386 True 0.027 1 0.632
TASK DETAIL
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To: Ladsgroup, AnotherLadsgroup
Cc: Halfak, Ladsgroup, Ricordisamoa, Aklapper, StudiesWorld, Lydia_Pintscher, samuwmde, AnotherLadsgroup, bkowshik, GoranSMilovanovic, QZanden, Avner, Izno, Wikidata-bugs, aude, He7d3r, Mbch331
Cc: Halfak, Ladsgroup, Ricordisamoa, Aklapper, StudiesWorld, Lydia_Pintscher, samuwmde, AnotherLadsgroup, bkowshik, GoranSMilovanovic, QZanden, Avner, Izno, Wikidata-bugs, aude, He7d3r, Mbch331
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