///////////////////////////////////////////
// Running CTAB-GAN on folding_abalone9-18
///////////////////////////////////////////

Load 'folding_abalone9-18'
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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 119, 19
LR fn, tp: 0, 9
LR f1 score: 0.486
LR cohens kappa score: 0.434
LR average precision score: 0.809

-> test with 'RF'
RF tn, fp: 131, 7
RF fn, tp: 2, 7
RF f1 score: 0.609
RF cohens kappa score: 0.577

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 6, 3
GB f1 score: 0.400
GB cohens kappa score: 0.369

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 3, 6
LR f1 score: 0.444
LR cohens kappa score: 0.395
LR average precision score: 0.606

-> test with 'RF'
RF tn, fp: 132, 6
RF fn, tp: 6, 3
RF f1 score: 0.333
RF cohens kappa score: 0.290

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 7, 2
GB f1 score: 0.308
GB cohens kappa score: 0.281

-> test with 'KNN'
KNN tn, fp: 134, 4
KNN fn, tp: 7, 2
KNN f1 score: 0.267
KNN cohens kappa score: 0.229


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 116, 22
LR fn, tp: 1, 8
LR f1 score: 0.410
LR cohens kappa score: 0.349
LR average precision score: 0.656

-> test with 'RF'
RF tn, fp: 129, 9
RF fn, tp: 5, 4
RF f1 score: 0.364
RF cohens kappa score: 0.314

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 6, 3
GB f1 score: 0.375
GB cohens kappa score: 0.340

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 7, 2
KNN f1 score: 0.364
KNN cohens kappa score: 0.349


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 122, 16
LR fn, tp: 2, 7
LR f1 score: 0.438
LR cohens kappa score: 0.383
LR average precision score: 0.608

-> test with 'RF'
RF tn, fp: 134, 4
RF fn, tp: 5, 4
RF f1 score: 0.471
RF cohens kappa score: 0.438

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 6, 3
GB f1 score: 0.429
GB cohens kappa score: 0.402

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 5, 4
KNN f1 score: 0.615
KNN cohens kappa score: 0.600


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

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-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 115, 22
LR fn, tp: 2, 4
LR f1 score: 0.250
LR cohens kappa score: 0.195
LR average precision score: 0.514

-> test with 'RF'
RF tn, fp: 130, 7
RF fn, tp: 3, 3
RF f1 score: 0.375
RF cohens kappa score: 0.340

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 5, 1
GB f1 score: 0.286
GB cohens kappa score: 0.277

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 4, 2
KNN f1 score: 0.500
KNN cohens kappa score: 0.489


====== 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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 115, 23
LR fn, tp: 2, 7
LR f1 score: 0.359
LR cohens kappa score: 0.292
LR average precision score: 0.664

-> test with 'RF'
RF tn, fp: 135, 3
RF fn, tp: 7, 2
RF f1 score: 0.286
RF cohens kappa score: 0.253

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.163

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 8, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.163


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 118, 20
LR fn, tp: 2, 7
LR f1 score: 0.389
LR cohens kappa score: 0.327
LR average precision score: 0.511

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 4, 5
RF f1 score: 0.526
RF cohens kappa score: 0.494

-> test with 'GB'
GB tn, fp: 138, 0
GB fn, tp: 7, 2
GB f1 score: 0.364
GB cohens kappa score: 0.349

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 6, 3
KNN f1 score: 0.462
KNN cohens kappa score: 0.440


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 117, 21
LR fn, tp: 2, 7
LR f1 score: 0.378
LR cohens kappa score: 0.315
LR average precision score: 0.674

-> test with 'RF'
RF tn, fp: 125, 13
RF fn, tp: 5, 4
RF f1 score: 0.308
RF cohens kappa score: 0.247

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104

-> test with 'KNN'
KNN tn, fp: 133, 5
KNN fn, tp: 7, 2
KNN f1 score: 0.250
KNN cohens kappa score: 0.208


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 121, 17
LR fn, tp: 1, 8
LR f1 score: 0.471
LR cohens kappa score: 0.418
LR average precision score: 0.718

-> test with 'RF'
RF tn, fp: 130, 8
RF fn, tp: 4, 5
RF f1 score: 0.455
RF cohens kappa score: 0.412

-> test with 'GB'
GB tn, fp: 136, 2
GB fn, tp: 5, 4
GB f1 score: 0.533
GB cohens kappa score: 0.509

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 6, 3
KNN f1 score: 0.462
KNN cohens kappa score: 0.440


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

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-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 120, 17
LR fn, tp: 1, 5
LR f1 score: 0.357
LR cohens kappa score: 0.312
LR average precision score: 0.640

-> test with 'RF'
RF tn, fp: 126, 11
RF fn, tp: 3, 3
RF f1 score: 0.300
RF cohens kappa score: 0.256

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 3, 3
GB f1 score: 0.500
GB cohens kappa score: 0.478

-> test with 'KNN'
KNN tn, fp: 136, 1
KNN fn, tp: 5, 1
KNN f1 score: 0.250
KNN cohens kappa score: 0.234


====== 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 518 synthetic samples
-> test with 'LR'
LR tn, fp: 119, 19
LR fn, tp: 4, 5
LR f1 score: 0.303
LR cohens kappa score: 0.235
LR average precision score: 0.404

-> test with 'RF'
RF tn, fp: 131, 7
RF fn, tp: 7, 2
RF f1 score: 0.222
RF cohens kappa score: 0.171

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 8, 1
GB f1 score: 0.154
GB cohens kappa score: 0.121

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 6, 3
KNN f1 score: 0.462
KNN cohens kappa score: 0.440


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 126, 12
LR fn, tp: 0, 9
LR f1 score: 0.600
LR cohens kappa score: 0.562
LR average precision score: 0.783

-> test with 'RF'
RF tn, fp: 132, 6
RF fn, tp: 5, 4
RF f1 score: 0.421
RF cohens kappa score: 0.381

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.012

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 6, 3
KNN f1 score: 0.400
KNN cohens kappa score: 0.369


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 125, 13
LR fn, tp: 4, 5
LR f1 score: 0.370
LR cohens kappa score: 0.314
LR average precision score: 0.533

-> test with 'RF'
RF tn, fp: 131, 7
RF fn, tp: 6, 3
RF f1 score: 0.316
RF cohens kappa score: 0.269

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 7, 2
GB f1 score: 0.267
GB cohens kappa score: 0.229

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.032


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 118, 20
LR fn, tp: 1, 8
LR f1 score: 0.432
LR cohens kappa score: 0.374
LR average precision score: 0.735

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 4, 5
RF f1 score: 0.526
RF cohens kappa score: 0.494

-> test with 'GB'
GB tn, fp: 138, 0
GB fn, tp: 5, 4
GB f1 score: 0.615
GB cohens kappa score: 0.600

-> test with 'KNN'
KNN tn, fp: 134, 4
KNN fn, tp: 6, 3
KNN f1 score: 0.375
KNN cohens kappa score: 0.340


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

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-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 111, 26
LR fn, tp: 1, 5
LR f1 score: 0.270
LR cohens kappa score: 0.215
LR average precision score: 0.596

-> test with 'RF'
RF tn, fp: 124, 13
RF fn, tp: 3, 3
RF f1 score: 0.273
RF cohens kappa score: 0.225

-> test with 'GB'
GB tn, fp: 135, 2
GB fn, tp: 4, 2
GB f1 score: 0.400
GB cohens kappa score: 0.379

-> test with 'KNN'
KNN tn, fp: 133, 4
KNN fn, tp: 5, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.149


====== 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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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 118, 20
LR fn, tp: 4, 5
LR f1 score: 0.294
LR cohens kappa score: 0.224
LR average precision score: 0.527

-> test with 'RF'
RF tn, fp: 130, 8
RF fn, tp: 5, 4
RF f1 score: 0.381
RF cohens kappa score: 0.334

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 6, 3
GB f1 score: 0.462
GB cohens kappa score: 0.440

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 6, 3
KNN f1 score: 0.400
KNN cohens kappa score: 0.369


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 111, 27
LR fn, tp: 1, 8
LR f1 score: 0.364
LR cohens kappa score: 0.295
LR average precision score: 0.574

-> test with 'RF'
RF tn, fp: 124, 14
RF fn, tp: 5, 4
RF f1 score: 0.296
RF cohens kappa score: 0.234

-> test with 'GB'
GB tn, fp: 138, 0
GB fn, tp: 6, 3
GB f1 score: 0.500
GB cohens kappa score: 0.484

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 6, 3
KNN f1 score: 0.462
KNN cohens kappa score: 0.440


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 130, 8
LR fn, tp: 1, 8
LR f1 score: 0.640
LR cohens kappa score: 0.609
LR average precision score: 0.778

-> test with 'RF'
RF tn, fp: 127, 11
RF fn, tp: 5, 4
RF f1 score: 0.333
RF cohens kappa score: 0.278

-> test with 'GB'
GB tn, fp: 138, 0
GB fn, tp: 7, 2
GB f1 score: 0.364
GB cohens kappa score: 0.349

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 5, 4
KNN f1 score: 0.615
KNN cohens kappa score: 0.600


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 114, 24
LR fn, tp: 3, 6
LR f1 score: 0.308
LR cohens kappa score: 0.236
LR average precision score: 0.564

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 5, 4
RF f1 score: 0.444
RF cohens kappa score: 0.408

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.163

-> test with 'KNN'
KNN tn, fp: 136, 2
KNN fn, tp: 8, 1
KNN f1 score: 0.167
KNN cohens kappa score: 0.140


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

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-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 123, 14
LR fn, tp: 1, 5
LR f1 score: 0.400
LR cohens kappa score: 0.359
LR average precision score: 0.523

-> test with 'RF'
RF tn, fp: 126, 11
RF fn, tp: 3, 3
RF f1 score: 0.300
RF cohens kappa score: 0.256

-> test with 'GB'
GB tn, fp: 134, 3
GB fn, tp: 4, 2
GB f1 score: 0.364
GB cohens kappa score: 0.338

-> test with 'KNN'
KNN tn, fp: 133, 4
KNN fn, tp: 5, 1
KNN f1 score: 0.182
KNN cohens kappa score: 0.149


====== 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%|██████████| 300/300 [02:08<00:00,  2.34it/s]
100%|██████████| 300/300 [02:08<00:00,  2.34it/s]
-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 106, 32
LR fn, tp: 0, 9
LR f1 score: 0.360
LR cohens kappa score: 0.289
LR average precision score: 0.439

-> test with 'RF'
RF tn, fp: 128, 10
RF fn, tp: 7, 2
RF f1 score: 0.190
RF cohens kappa score: 0.130

-> test with 'GB'
GB tn, fp: 134, 4
GB fn, tp: 8, 1
GB f1 score: 0.143
GB cohens kappa score: 0.104

-> test with 'KNN'
KNN tn, fp: 132, 6
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.052


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 116, 22
LR fn, tp: 4, 5
LR f1 score: 0.278
LR cohens kappa score: 0.205
LR average precision score: 0.467

-> test with 'RF'
RF tn, fp: 133, 5
RF fn, tp: 6, 3
RF f1 score: 0.353
RF cohens kappa score: 0.313

-> test with 'GB'
GB tn, fp: 137, 1
GB fn, tp: 7, 2
GB f1 score: 0.333
GB cohens kappa score: 0.312

-> test with 'KNN'
KNN tn, fp: 137, 1
KNN fn, tp: 7, 2
KNN f1 score: 0.333
KNN cohens kappa score: 0.312


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 123, 15
LR fn, tp: 3, 6
LR f1 score: 0.400
LR cohens kappa score: 0.344
LR average precision score: 0.619

-> test with 'RF'
RF tn, fp: 132, 6
RF fn, tp: 5, 4
RF f1 score: 0.421
RF cohens kappa score: 0.381

-> test with 'GB'
GB tn, fp: 135, 3
GB fn, tp: 7, 2
GB f1 score: 0.286
GB cohens kappa score: 0.253

-> test with 'KNN'
KNN tn, fp: 135, 3
KNN fn, tp: 6, 3
KNN f1 score: 0.400
KNN cohens kappa score: 0.369


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

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-> create 518 synthetic samples
-> test with 'LR'
LR tn, fp: 116, 22
LR fn, tp: 1, 8
LR f1 score: 0.410
LR cohens kappa score: 0.349
LR average precision score: 0.751

-> test with 'RF'
RF tn, fp: 128, 10
RF fn, tp: 3, 6
RF f1 score: 0.480
RF cohens kappa score: 0.436

-> test with 'GB'
GB tn, fp: 138, 0
GB fn, tp: 6, 3
GB f1 score: 0.500
GB cohens kappa score: 0.484

-> test with 'KNN'
KNN tn, fp: 138, 0
KNN fn, tp: 6, 3
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


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

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-> create 516 synthetic samples
-> test with 'LR'
LR tn, fp: 121, 16
LR fn, tp: 2, 4
LR f1 score: 0.308
LR cohens kappa score: 0.260
LR average precision score: 0.610

-> test with 'RF'
RF tn, fp: 132, 5
RF fn, tp: 3, 3
RF f1 score: 0.429
RF cohens kappa score: 0.400

-> test with 'GB'
GB tn, fp: 137, 0
GB fn, tp: 4, 2
GB f1 score: 0.500
GB cohens kappa score: 0.489

-> test with 'KNN'
KNN tn, fp: 137, 0
KNN fn, tp: 5, 1
KNN f1 score: 0.286
KNN cohens kappa score: 0.277

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 130, 32
LR fn, tp: 4, 9
LR f1 score: 0.640
LR cohens kappa score: 0.609
LR average precision score: 0.809


average:
LR tn, fp: 118.64, 19.16
LR fn, tp: 1.84, 6.56
LR f1 score: 0.389
LR cohens kappa score: 0.332
LR average precision score: 0.612


minimum:
LR tn, fp: 106, 8
LR fn, tp: 0, 4
LR f1 score: 0.250
LR cohens kappa score: 0.195
LR average precision score: 0.404


-----[ RF ]-----
maximum:
RF tn, fp: 135, 14
RF fn, tp: 7, 7
RF f1 score: 0.609
RF cohens kappa score: 0.577


average:
RF tn, fp: 129.96, 7.84
RF fn, tp: 4.64, 3.76
RF f1 score: 0.376
RF cohens kappa score: 0.333


minimum:
RF tn, fp: 124, 3
RF fn, tp: 2, 2
RF f1 score: 0.190
RF cohens kappa score: 0.130


-----[ GB ]-----
maximum:
GB tn, fp: 138, 4
GB fn, tp: 9, 4
GB f1 score: 0.615
GB cohens kappa score: 0.600


average:
GB tn, fp: 136.04, 1.76
GB fn, tp: 6.28, 2.12
GB f1 score: 0.343
GB cohens kappa score: 0.320


minimum:
GB tn, fp: 134, 0
GB fn, tp: 3, 0
GB f1 score: 0.000
GB cohens kappa score: -0.012


-----[ KNN ]-----
maximum:
KNN tn, fp: 138, 6
KNN fn, tp: 9, 4
KNN f1 score: 0.615
KNN cohens kappa score: 0.600


average:
KNN tn, fp: 135.84, 1.96
KNN fn, tp: 6.28, 2.12
KNN f1 score: 0.338
KNN cohens kappa score: 0.313


minimum:
KNN tn, fp: 132, 0
KNN fn, tp: 4, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.052

  wall time: 00:53:59s, process time: 06:59:24s