ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_car-vgood.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_car-vgood
///////////////////////////////////////////

Load 'data_input/folding_car-vgood'
from pickle file
Data loaded.
-> Shuffling data
### Start exercise for synthetic point generator

====== Step 1/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 1/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 295, 38
LR fn, tp: 0, 13
LR f1 score: 0.406
LR cohens kappa score: 0.368
LR average precision score: 0.335

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 322, 11
KNN fn, tp: 0, 13
KNN f1 score: 0.703
KNN cohens kappa score: 0.687


------ Step 1/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 314, 19
LR fn, tp: 6, 7
LR f1 score: 0.359
LR cohens kappa score: 0.325
LR average precision score: 0.274

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 312, 21
KNN fn, tp: 0, 13
KNN f1 score: 0.553
KNN cohens kappa score: 0.528


------ Step 1/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 302, 31
LR fn, tp: 1, 12
LR f1 score: 0.429
LR cohens kappa score: 0.394
LR average precision score: 0.397

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 313, 20
KNN fn, tp: 0, 13
KNN f1 score: 0.565
KNN cohens kappa score: 0.540


------ Step 1/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 50
LR fn, tp: 0, 13
LR f1 score: 0.342
LR cohens kappa score: 0.298
LR average precision score: 0.392

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 320, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


------ Step 1/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 302, 29
LR fn, tp: 3, 10
LR f1 score: 0.385
LR cohens kappa score: 0.348
LR average precision score: 0.403

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 318, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


====== Step 2/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 2/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 279, 54
LR fn, tp: 0, 13
LR f1 score: 0.325
LR cohens kappa score: 0.280
LR average precision score: 0.259

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 310, 23
KNN fn, tp: 0, 13
KNN f1 score: 0.531
KNN cohens kappa score: 0.503


------ Step 2/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 305, 28
LR fn, tp: 5, 8
LR f1 score: 0.327
LR cohens kappa score: 0.287
LR average precision score: 0.274

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 313, 20
KNN fn, tp: 3, 10
KNN f1 score: 0.465
KNN cohens kappa score: 0.436


------ Step 2/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 311, 22
LR fn, tp: 4, 9
LR f1 score: 0.409
LR cohens kappa score: 0.376
LR average precision score: 0.355

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 1, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 319, 14
KNN fn, tp: 0, 13
KNN f1 score: 0.650
KNN cohens kappa score: 0.631


------ Step 2/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 16
LR fn, tp: 6, 7
LR f1 score: 0.389
LR cohens kappa score: 0.358
LR average precision score: 0.357

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 2, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914

-> test with 'KNN'
KNN tn, fp: 322, 11
KNN fn, tp: 0, 13
KNN f1 score: 0.703
KNN cohens kappa score: 0.687


------ Step 2/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 13
LR fn, tp: 4, 9
LR f1 score: 0.514
LR cohens kappa score: 0.490
LR average precision score: 0.434

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.958

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 326, 5
KNN fn, tp: 6, 7
KNN f1 score: 0.560
KNN cohens kappa score: 0.543


====== Step 3/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 3/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 47
LR fn, tp: 0, 13
LR f1 score: 0.356
LR cohens kappa score: 0.314
LR average precision score: 0.312

-> test with 'RF'
RF tn, fp: 332, 1
RF fn, tp: 3, 10
RF f1 score: 0.833
RF cohens kappa score: 0.827

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 320, 13
KNN fn, tp: 1, 12
KNN f1 score: 0.632
KNN cohens kappa score: 0.612


------ Step 3/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 291, 42
LR fn, tp: 0, 13
LR f1 score: 0.382
LR cohens kappa score: 0.342
LR average precision score: 0.397

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 15
KNN fn, tp: 1, 12
KNN f1 score: 0.600
KNN cohens kappa score: 0.579


------ Step 3/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 297, 36
LR fn, tp: 2, 11
LR f1 score: 0.367
LR cohens kappa score: 0.327
LR average precision score: 0.327

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 331, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 307, 26
KNN fn, tp: 0, 13
KNN f1 score: 0.500
KNN cohens kappa score: 0.470


------ Step 3/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:06<00:00,  1.60it/s]
100%|██████████| 10/10 [00:06<00:00,  1.61it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 294, 39
LR fn, tp: 1, 12
LR f1 score: 0.375
LR cohens kappa score: 0.335
LR average precision score: 0.332

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 318, 15
KNN fn, tp: 2, 11
KNN f1 score: 0.564
KNN cohens kappa score: 0.541


------ Step 3/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:07<00:00,  1.19it/s]
100%|██████████| 10/10 [00:07<00:00,  1.28it/s]
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 317, 14
LR fn, tp: 8, 5
LR f1 score: 0.312
LR cohens kappa score: 0.280
LR average precision score: 0.370

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.958

-> test with 'KNN'
KNN tn, fp: 320, 11
KNN fn, tp: 0, 13
KNN f1 score: 0.703
KNN cohens kappa score: 0.687


====== Step 4/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 4/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:05<00:00,  1.71it/s]
100%|██████████| 10/10 [00:05<00:00,  1.69it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 318, 15
LR fn, tp: 5, 8
LR f1 score: 0.444
LR cohens kappa score: 0.416
LR average precision score: 0.355

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 315, 18
KNN fn, tp: 1, 12
KNN f1 score: 0.558
KNN cohens kappa score: 0.534


------ Step 4/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:06<00:00,  1.38it/s]
100%|██████████| 10/10 [00:06<00:00,  1.56it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 303, 30
LR fn, tp: 1, 12
LR f1 score: 0.436
LR cohens kappa score: 0.402
LR average precision score: 0.506

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 316, 17
KNN fn, tp: 0, 13
KNN f1 score: 0.605
KNN cohens kappa score: 0.583


------ Step 4/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:06<00:00,  1.49it/s]
100%|██████████| 10/10 [00:06<00:00,  1.57it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 272, 61
LR fn, tp: 0, 13
LR f1 score: 0.299
LR cohens kappa score: 0.251
LR average precision score: 0.316

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 0, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 328, 5
KNN fn, tp: 0, 13
KNN f1 score: 0.839
KNN cohens kappa score: 0.831


------ Step 4/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:08<00:00,  1.05it/s]
100%|██████████| 10/10 [00:08<00:00,  1.16it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 313, 20
LR fn, tp: 3, 10
LR f1 score: 0.465
LR cohens kappa score: 0.436
LR average precision score: 0.288

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 332, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 318, 15
KNN fn, tp: 0, 13
KNN f1 score: 0.634
KNN cohens kappa score: 0.614


------ Step 4/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:06<00:00,  1.69it/s]
100%|██████████| 10/10 [00:06<00:00,  1.60it/s]
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 284, 47
LR fn, tp: 0, 13
LR f1 score: 0.356
LR cohens kappa score: 0.314
LR average precision score: 0.335

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 2, 11
RF f1 score: 0.917
RF cohens kappa score: 0.914

-> test with 'GB'
GB tn, fp: 331, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 312, 19
KNN fn, tp: 1, 12
KNN f1 score: 0.545
KNN cohens kappa score: 0.520


====== Step 5/5 =======
-> Shuffling data
-> Spliting data to slices

------ Step 5/5: Slice 1/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:06<00:00,  1.60it/s]
100%|██████████| 10/10 [00:06<00:00,  1.58it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 276, 57
LR fn, tp: 0, 13
LR f1 score: 0.313
LR cohens kappa score: 0.267
LR average precision score: 0.261

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 3, 10
RF f1 score: 0.870
RF cohens kappa score: 0.865

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 317, 16
KNN fn, tp: 1, 12
KNN f1 score: 0.585
KNN cohens kappa score: 0.563


------ Step 5/5: Slice 2/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:05<00:00,  1.80it/s]
100%|██████████| 10/10 [00:05<00:00,  1.82it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 52
LR fn, tp: 0, 13
LR f1 score: 0.333
LR cohens kappa score: 0.289
LR average precision score: 0.331

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 3, 10
RF f1 score: 0.870
RF cohens kappa score: 0.865

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 323, 10
KNN fn, tp: 0, 13
KNN f1 score: 0.722
KNN cohens kappa score: 0.708


------ Step 5/5: Slice 3/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

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100%|██████████| 10/10 [00:05<00:00,  1.90it/s]
100%|██████████| 10/10 [00:05<00:00,  1.94it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 307, 26
LR fn, tp: 3, 10
LR f1 score: 0.408
LR cohens kappa score: 0.374
LR average precision score: 0.361

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 0, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 316, 17
KNN fn, tp: 0, 13
KNN f1 score: 0.605
KNN cohens kappa score: 0.583


------ Step 5/5: Slice 4/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
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 20%|██        | 2/10 [00:01<00:04,  1.92it/s]
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 60%|██████    | 6/10 [00:03<00:02,  1.75it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.81it/s]
100%|██████████| 10/10 [00:05<00:00,  1.79it/s]
-> create 1278 synthetic samples
-> test with 'LR'
LR tn, fp: 314, 19
LR fn, tp: 8, 5
LR f1 score: 0.270
LR cohens kappa score: 0.233
LR average precision score: 0.289

-> test with 'RF'
RF tn, fp: 333, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.959

-> test with 'GB'
GB tn, fp: 333, 0
GB fn, tp: 1, 12
GB f1 score: 0.960
GB cohens kappa score: 0.959

-> test with 'KNN'
KNN tn, fp: 317, 16
KNN fn, tp: 0, 13
KNN f1 score: 0.619
KNN cohens kappa score: 0.598


------ Step 5/5: Slice 5/5 -------
-> Reset the GAN
-> Train generator for synthetic samples

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:05,  1.74it/s]
 20%|██        | 2/10 [00:01<00:04,  1.81it/s]
 30%|███       | 3/10 [00:01<00:03,  1.81it/s]
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 60%|██████    | 6/10 [00:03<00:02,  1.85it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.83it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.78it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.79it/s]
100%|██████████| 10/10 [00:05<00:00,  1.79it/s]
100%|██████████| 10/10 [00:05<00:00,  1.81it/s]
-> create 1280 synthetic samples
-> test with 'LR'
LR tn, fp: 311, 20
LR fn, tp: 3, 10
LR f1 score: 0.465
LR cohens kappa score: 0.435
LR average precision score: 0.447

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 1, 12
RF f1 score: 0.960
RF cohens kappa score: 0.958

-> test with 'GB'
GB tn, fp: 329, 2
GB fn, tp: 0, 13
GB f1 score: 0.929
GB cohens kappa score: 0.926

-> test with 'KNN'
KNN tn, fp: 318, 13
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.649

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 318, 61
LR fn, tp: 8, 13
LR f1 score: 0.514
LR cohens kappa score: 0.490
LR average precision score: 0.506


average:
LR tn, fp: 299.6, 33.0
LR fn, tp: 2.52, 10.48
LR f1 score: 0.379
LR cohens kappa score: 0.342
LR average precision score: 0.348


minimum:
LR tn, fp: 272, 13
LR fn, tp: 0, 5
LR f1 score: 0.270
LR cohens kappa score: 0.233
LR average precision score: 0.259


-----[ RF ]-----
maximum:
RF tn, fp: 333, 1
RF fn, tp: 3, 13
RF f1 score: 1.000
RF cohens kappa score: 1.000


average:
RF tn, fp: 332.56, 0.04
RF fn, tp: 1.32, 11.68
RF f1 score: 0.944
RF cohens kappa score: 0.942


minimum:
RF tn, fp: 331, 0
RF fn, tp: 0, 10
RF f1 score: 0.833
RF cohens kappa score: 0.827


-----[ GB ]-----
maximum:
GB tn, fp: 333, 2
GB fn, tp: 2, 13
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 332.2, 0.4
GB fn, tp: 0.28, 12.72
GB f1 score: 0.974
GB cohens kappa score: 0.973


minimum:
GB tn, fp: 329, 0
GB fn, tp: 0, 11
GB f1 score: 0.917
GB cohens kappa score: 0.914


-----[ KNN ]-----
maximum:
KNN tn, fp: 328, 26
KNN fn, tp: 6, 13
KNN f1 score: 0.839
KNN cohens kappa score: 0.831


average:
KNN tn, fp: 317.52, 15.08
KNN fn, tp: 0.64, 12.36
KNN f1 score: 0.618
KNN cohens kappa score: 0.597


minimum:
KNN tn, fp: 307, 5
KNN fn, tp: 0, 7
KNN f1 score: 0.465
KNN cohens kappa score: 0.436