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har-rf-0.log
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har-rf-0.log
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Data set: har
Algorithm: rf
Loading har data set...
Creating random forest hyper classifier...
Running trial 0...
Creating trial data...
Applying classifier to trial...
No cached result "runs/har/0/rf/estimators(500)-split(0.5).pickle", building and running classifier...
561
23.6854385647
0.5
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run: estimators(500)-split(0.5)
parameters: {'n_estimators': 500, 'split': 0.5}
ml ce: 0.0730326476694
ml rmse: 0.376714173609
sk acc: 0.926967352331
sk f1: 0.926334910101
sk prec: 0.928826645846
sk recl: 0.926967352331
label: 1
ml auc: 0.981010234423
sk auc: 0.981010234423
label: 2
ml auc: 0.933551163412
sk auc: 0.933551163412
label: 3
ml auc: 0.896806938485
sk auc: 0.896806938485
label: 4
ml auc: 0.95099197945
sk auc: 0.95099197945
label: 5
ml auc: 0.960200624203
sk auc: 0.960200624203
label: 6
ml auc: 1.0
sk auc: 1.0
Saving result "runs/har/0/rf/estimators(500)-split(0.5).pickle"...
No cached result "runs/har/0/rf/estimators(500)-split(1).pickle", building and running classifier...
561
23.6854385647
1
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run: estimators(500)-split(1)
parameters: {'n_estimators': 500, 'split': 1}
ml ce: 0.0871862615588
ml rmse: 0.406124094982
sk acc: 0.912813738441
sk f1: 0.912361924857
sk prec: 0.914818271883
sk recl: 0.912813738441
label: 1
ml auc: 0.976229831663
sk auc: 0.976229831663
label: 2
ml auc: 0.923001802602
sk auc: 0.923001802602
label: 3
ml auc: 0.904863993564
sk auc: 0.904863993564
label: 4
ml auc: 0.943834515237
sk auc: 0.943834515237
label: 5
ml auc: 0.927029089216
sk auc: 0.927029089216
label: 6
ml auc: 1.0
sk auc: 1.0
Saving result "runs/har/0/rf/estimators(500)-split(1).pickle"...
No cached result "runs/har/0/rf/estimators(500)-split(2).pickle", building and running classifier...
561
23.6854385647
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run: estimators(500)-split(2)
parameters: {'n_estimators': 500, 'split': 2}
ml ce: 0.0934138516701
ml rmse: 0.413492026264
sk acc: 0.90658614833
sk f1: 0.906310417616
sk prec: 0.909043679749
sk recl: 0.90658614833
label: 1
ml auc: 0.975797088147
sk auc: 0.975797088147
label: 2
ml auc: 0.919126584803
sk auc: 0.919126584803
label: 3
ml auc: 0.915680090477
sk auc: 0.915680090477
label: 4
ml auc: 0.931642383362
sk auc: 0.931642383362
label: 5
ml auc: 0.912723011576
sk auc: 0.912723011576
label: 6
ml auc: 1.0
sk auc: 1.0
Saving result "runs/har/0/rf/estimators(500)-split(2).pickle"...
No cached result "runs/har/0/rf/estimators(500)-split(4).pickle", building and running classifier...
561
23.6854385647
4
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run: estimators(500)-split(4)
parameters: {'n_estimators': 500, 'split': 4}
ml ce: 0.0966220041517
ml rmse: 0.407515729311
sk acc: 0.903377995848
sk f1: 0.903139217905
sk prec: 0.90606640944
sk recl: 0.903377995848
label: 1
ml auc: 0.975241963001
sk auc: 0.975241963001
label: 2
ml auc: 0.915108363094
sk auc: 0.915108363094
label: 3
ml auc: 0.920095781644
sk auc: 0.920095781644
label: 4
ml auc: 0.92616554549
sk auc: 0.92616554549
label: 5
ml auc: 0.90770417342
sk auc: 0.90770417342
label: 6
ml auc: 1.0
sk auc: 1.0
Saving result "runs/har/0/rf/estimators(500)-split(4).pickle"...
No cached result "runs/har/0/rf/estimators(500)-split(8).pickle", building and running classifier...
561
23.6854385647
8
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run: estimators(500)-split(8)
parameters: {'n_estimators': 500, 'split': 8}
ml ce: 0.100396301189
ml rmse: 0.386847356879
sk acc: 0.899603698811
sk f1: 0.899387387342
sk prec: 0.902387796955
sk recl: 0.899603698811
label: 1
ml auc: 0.972277708808
sk auc: 0.972277708808
label: 2
ml auc: 0.909970571301
sk auc: 0.909970571301
label: 3
ml auc: 0.923076934288
sk auc: 0.923076934288
label: 4
ml auc: 0.921029539247
sk auc: 0.921029539247
label: 5
ml auc: 0.904934849582
sk auc: 0.904934849582
label: 6
ml auc: 1.0
sk auc: 1.0
Saving result "runs/har/0/rf/estimators(500)-split(8).pickle"...
Clearing trial data from memory...