ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_yeast6.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_yeast6
///////////////////////////////////////////

Load 'data_input/folding_yeast6'
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 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 9
LR fn, tp: 1, 6
LR f1 score: 0.545
LR cohens kappa score: 0.530
LR average precision score: 0.692

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 4, 3
RF f1 score: 0.545
RF cohens kappa score: 0.538

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 3, 4
GB f1 score: 0.571
GB cohens kappa score: 0.561

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 3, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.310


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 274, 16
LR fn, tp: 3, 4
LR f1 score: 0.296
LR cohens kappa score: 0.271
LR average precision score: 0.286

-> test with 'RF'
RF tn, fp: 287, 3
RF fn, tp: 5, 2
RF f1 score: 0.333
RF cohens kappa score: 0.320

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 2, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.379


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 4
LR fn, tp: 4, 3
LR f1 score: 0.429
LR cohens kappa score: 0.415
LR average precision score: 0.387

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 5, 2
RF f1 score: 0.444
RF cohens kappa score: 0.439

-> test with 'GB'
GB tn, fp: 290, 0
GB fn, tp: 3, 4
GB f1 score: 0.727
GB cohens kappa score: 0.723

-> test with 'KNN'
KNN tn, fp: 284, 6
KNN fn, tp: 2, 5
KNN f1 score: 0.556
KNN cohens kappa score: 0.542


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 265, 25
LR fn, tp: 1, 6
LR f1 score: 0.316
LR cohens kappa score: 0.288
LR average precision score: 0.533

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 4, 3
RF f1 score: 0.545
RF cohens kappa score: 0.538

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 284, 6
KNN fn, tp: 4, 3
KNN f1 score: 0.375
KNN cohens kappa score: 0.358


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

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-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 249, 40
LR fn, tp: 0, 7
LR f1 score: 0.259
LR cohens kappa score: 0.227
LR average precision score: 0.379

-> test with 'RF'
RF tn, fp: 288, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 286, 3
GB fn, tp: 4, 3
GB f1 score: 0.462
GB cohens kappa score: 0.450

-> test with 'KNN'
KNN tn, fp: 279, 10
KNN fn, tp: 1, 6
KNN f1 score: 0.522
KNN cohens kappa score: 0.505


====== 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 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 267, 23
LR fn, tp: 1, 6
LR f1 score: 0.333
LR cohens kappa score: 0.307
LR average precision score: 0.605

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 271, 19
KNN fn, tp: 3, 4
KNN f1 score: 0.267
KNN cohens kappa score: 0.239


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 260, 30
LR fn, tp: 0, 7
LR f1 score: 0.318
LR cohens kappa score: 0.290
LR average precision score: 0.289

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 278, 12
KNN fn, tp: 0, 7
KNN f1 score: 0.538
KNN cohens kappa score: 0.522


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 259, 31
LR fn, tp: 1, 6
LR f1 score: 0.273
LR cohens kappa score: 0.243
LR average precision score: 0.393

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 6, 1
RF f1 score: 0.222
RF cohens kappa score: 0.214

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 4, 3
GB f1 score: 0.500
GB cohens kappa score: 0.490

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 3, 4
KNN f1 score: 0.333
KNN cohens kappa score: 0.310


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 265, 25
LR fn, tp: 2, 5
LR f1 score: 0.270
LR cohens kappa score: 0.241
LR average precision score: 0.338

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 285, 5
GB fn, tp: 5, 2
GB f1 score: 0.286
GB cohens kappa score: 0.268

-> test with 'KNN'
KNN tn, fp: 272, 18
KNN fn, tp: 4, 3
KNN f1 score: 0.214
KNN cohens kappa score: 0.185


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

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-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 280, 9
LR fn, tp: 3, 4
LR f1 score: 0.400
LR cohens kappa score: 0.381
LR average precision score: 0.478

-> test with 'RF'
RF tn, fp: 289, 0
RF fn, tp: 5, 2
RF f1 score: 0.444
RF cohens kappa score: 0.439

-> test with 'GB'
GB tn, fp: 289, 0
GB fn, tp: 6, 1
GB f1 score: 0.250
GB cohens kappa score: 0.246

-> test with 'KNN'
KNN tn, fp: 281, 8
KNN fn, tp: 3, 4
KNN f1 score: 0.421
KNN cohens kappa score: 0.403


====== 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 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 275, 15
LR fn, tp: 2, 5
LR f1 score: 0.370
LR cohens kappa score: 0.348
LR average precision score: 0.517

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 3, 4
GB f1 score: 0.667
GB cohens kappa score: 0.660

-> test with 'KNN'
KNN tn, fp: 275, 15
KNN fn, tp: 2, 5
KNN f1 score: 0.370
KNN cohens kappa score: 0.348


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 266, 24
LR fn, tp: 0, 7
LR f1 score: 0.368
LR cohens kappa score: 0.343
LR average precision score: 0.429

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 3, 4
RF f1 score: 0.727
RF cohens kappa score: 0.723

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.538

-> test with 'KNN'
KNN tn, fp: 282, 8
KNN fn, tp: 2, 5
KNN f1 score: 0.500
KNN cohens kappa score: 0.484


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

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-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 4
LR fn, tp: 5, 2
LR f1 score: 0.308
LR cohens kappa score: 0.292
LR average precision score: 0.501

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 6, 1
RF f1 score: 0.222
RF cohens kappa score: 0.214

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 6, 1
GB f1 score: 0.222
GB cohens kappa score: 0.214

-> test with 'KNN'
KNN tn, fp: 285, 5
KNN fn, tp: 3, 4
KNN f1 score: 0.500
KNN cohens kappa score: 0.486


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

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100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
100%|██████████| 10/10 [00:05<00:00,  1.95it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 249, 41
LR fn, tp: 1, 6
LR f1 score: 0.222
LR cohens kappa score: 0.189
LR average precision score: 0.262

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 4, 3
RF f1 score: 0.500
RF cohens kappa score: 0.490

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 4, 3
GB f1 score: 0.429
GB cohens kappa score: 0.415

-> test with 'KNN'
KNN tn, fp: 274, 16
KNN fn, tp: 2, 5
KNN f1 score: 0.357
KNN cohens kappa score: 0.334


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

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100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 285, 4
LR fn, tp: 2, 5
LR f1 score: 0.625
LR cohens kappa score: 0.615
LR average precision score: 0.529

-> test with 'RF'
RF tn, fp: 289, 0
RF fn, tp: 7, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 7, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006

-> test with 'KNN'
KNN tn, fp: 285, 4
KNN fn, tp: 2, 5
KNN f1 score: 0.625
KNN cohens kappa score: 0.615


====== 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,  1.92it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 257, 33
LR fn, tp: 0, 7
LR f1 score: 0.298
LR cohens kappa score: 0.269
LR average precision score: 0.567

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 5, 2
RF f1 score: 0.444
RF cohens kappa score: 0.439

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 3, 4
GB f1 score: 0.667
GB cohens kappa score: 0.660

-> test with 'KNN'
KNN tn, fp: 280, 10
KNN fn, tp: 1, 6
KNN f1 score: 0.522
KNN cohens kappa score: 0.506


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

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100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
100%|██████████| 10/10 [00:05<00:00,  1.93it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 254, 36
LR fn, tp: 2, 5
LR f1 score: 0.208
LR cohens kappa score: 0.175
LR average precision score: 0.162

-> test with 'RF'
RF tn, fp: 288, 2
RF fn, tp: 4, 3
RF f1 score: 0.500
RF cohens kappa score: 0.490

-> test with 'GB'
GB tn, fp: 287, 3
GB fn, tp: 5, 2
GB f1 score: 0.333
GB cohens kappa score: 0.320

-> test with 'KNN'
KNN tn, fp: 277, 13
KNN fn, tp: 2, 5
KNN f1 score: 0.400
KNN cohens kappa score: 0.379


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

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100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 9
LR fn, tp: 2, 5
LR f1 score: 0.476
LR cohens kappa score: 0.459
LR average precision score: 0.447

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 3, 4
RF f1 score: 0.667
RF cohens kappa score: 0.660

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 2, 5
GB f1 score: 0.625
GB cohens kappa score: 0.615

-> test with 'KNN'
KNN tn, fp: 278, 12
KNN fn, tp: 2, 5
KNN f1 score: 0.417
KNN cohens kappa score: 0.397


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

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100%|██████████| 10/10 [00:05<00:00,  1.84it/s]
100%|██████████| 10/10 [00:05<00:00,  1.81it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 4
LR fn, tp: 2, 5
LR f1 score: 0.625
LR cohens kappa score: 0.615
LR average precision score: 0.646

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 4, 3
RF f1 score: 0.600
RF cohens kappa score: 0.594

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 285, 5
KNN fn, tp: 3, 4
KNN f1 score: 0.500
KNN cohens kappa score: 0.486


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

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100%|██████████| 10/10 [00:05<00:00,  1.92it/s]
100%|██████████| 10/10 [00:05<00:00,  1.88it/s]
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 264, 25
LR fn, tp: 2, 5
LR f1 score: 0.270
LR cohens kappa score: 0.241
LR average precision score: 0.564

-> test with 'RF'
RF tn, fp: 289, 0
RF fn, tp: 4, 3
RF f1 score: 0.600
RF cohens kappa score: 0.594

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 4, 3
GB f1 score: 0.545
GB cohens kappa score: 0.537

-> test with 'KNN'
KNN tn, fp: 276, 13
KNN fn, tp: 4, 3
KNN f1 score: 0.261
KNN cohens kappa score: 0.236


====== 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 [00:05<00:00,  1.99it/s]
100%|██████████| 10/10 [00:05<00:00,  1.97it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 283, 7
LR fn, tp: 2, 5
LR f1 score: 0.526
LR cohens kappa score: 0.512
LR average precision score: 0.315

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 3, 4
RF f1 score: 0.667
RF cohens kappa score: 0.660

-> test with 'GB'
GB tn, fp: 286, 4
GB fn, tp: 3, 4
GB f1 score: 0.533
GB cohens kappa score: 0.521

-> test with 'KNN'
KNN tn, fp: 278, 12
KNN fn, tp: 1, 6
KNN f1 score: 0.480
KNN cohens kappa score: 0.462


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

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100%|██████████| 10/10 [00:06<00:00,  1.25it/s]
100%|██████████| 10/10 [00:06<00:00,  1.61it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 288, 2
LR fn, tp: 6, 1
LR f1 score: 0.200
LR cohens kappa score: 0.189
LR average precision score: 0.210

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 288, 2
GB fn, tp: 5, 2
GB f1 score: 0.364
GB cohens kappa score: 0.353

-> test with 'KNN'
KNN tn, fp: 282, 8
KNN fn, tp: 4, 3
KNN f1 score: 0.333
KNN cohens kappa score: 0.314


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

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 10%|█         | 1/10 [00:01<00:13,  1.45s/it]
 20%|██        | 2/10 [00:01<00:07,  1.09it/s]
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 50%|█████     | 5/10 [00:03<00:03,  1.56it/s]
 60%|██████    | 6/10 [00:04<00:02,  1.60it/s]
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100%|██████████| 10/10 [00:06<00:00,  1.72it/s]
100%|██████████| 10/10 [00:06<00:00,  1.52it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 289, 1
LR fn, tp: 4, 3
LR f1 score: 0.545
LR cohens kappa score: 0.538
LR average precision score: 0.467

-> test with 'RF'
RF tn, fp: 289, 1
RF fn, tp: 2, 5
RF f1 score: 0.769
RF cohens kappa score: 0.764

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 2, 5
GB f1 score: 0.769
GB cohens kappa score: 0.764

-> test with 'KNN'
KNN tn, fp: 278, 12
KNN fn, tp: 1, 6
KNN f1 score: 0.480
KNN cohens kappa score: 0.462


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

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 10%|█         | 1/10 [00:00<00:04,  1.91it/s]
 20%|██        | 2/10 [00:01<00:04,  1.88it/s]
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 50%|█████     | 5/10 [00:02<00:02,  1.89it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.89it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
100%|██████████| 10/10 [00:05<00:00,  1.89it/s]
-> create 1131 synthetic samples
-> test with 'LR'
LR tn, fp: 287, 3
LR fn, tp: 4, 3
LR f1 score: 0.462
LR cohens kappa score: 0.450
LR average precision score: 0.391

-> test with 'RF'
RF tn, fp: 290, 0
RF fn, tp: 5, 2
RF f1 score: 0.444
RF cohens kappa score: 0.439

-> test with 'GB'
GB tn, fp: 289, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 284, 6
KNN fn, tp: 3, 4
KNN f1 score: 0.471
KNN cohens kappa score: 0.455


------ Step 5/5: Slice 5/5 -------
-> 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,  1.82it/s]
 30%|███       | 3/10 [00:01<00:03,  1.86it/s]
 40%|████      | 4/10 [00:02<00:03,  1.86it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.92it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.96it/s]
 70%|███████   | 7/10 [00:03<00:01,  1.93it/s]
 80%|████████  | 8/10 [00:04<00:01,  1.95it/s]
 90%|█████████ | 9/10 [00:04<00:00,  1.93it/s]
100%|██████████| 10/10 [00:05<00:00,  1.92it/s]
100%|██████████| 10/10 [00:05<00:00,  1.91it/s]
-> create 1132 synthetic samples
-> test with 'LR'
LR tn, fp: 281, 8
LR fn, tp: 3, 4
LR f1 score: 0.421
LR cohens kappa score: 0.403
LR average precision score: 0.316

-> test with 'RF'
RF tn, fp: 288, 1
RF fn, tp: 5, 2
RF f1 score: 0.400
RF cohens kappa score: 0.391

-> test with 'GB'
GB tn, fp: 288, 1
GB fn, tp: 5, 2
GB f1 score: 0.400
GB cohens kappa score: 0.391

-> test with 'KNN'
KNN tn, fp: 285, 4
KNN fn, tp: 4, 3
KNN f1 score: 0.429
KNN cohens kappa score: 0.415

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 289, 41
LR fn, tp: 6, 7
LR f1 score: 0.625
LR cohens kappa score: 0.615
LR average precision score: 0.692


average:
LR tn, fp: 272.68, 17.12
LR fn, tp: 2.12, 4.88
LR f1 score: 0.375
LR cohens kappa score: 0.353
LR average precision score: 0.428


minimum:
LR tn, fp: 249, 1
LR fn, tp: 0, 1
LR f1 score: 0.200
LR cohens kappa score: 0.175
LR average precision score: 0.162


-----[ RF ]-----
maximum:
RF tn, fp: 290, 3
RF fn, tp: 7, 5
RF f1 score: 0.769
RF cohens kappa score: 0.764


average:
RF tn, fp: 288.96, 0.84
RF fn, tp: 4.56, 2.44
RF f1 score: 0.459
RF cohens kappa score: 0.452


minimum:
RF tn, fp: 287, 0
RF fn, tp: 2, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000


-----[ GB ]-----
maximum:
GB tn, fp: 290, 5
GB fn, tp: 7, 5
GB f1 score: 0.769
GB cohens kappa score: 0.764


average:
GB tn, fp: 287.76, 2.04
GB fn, tp: 4.16, 2.84
GB f1 score: 0.463
GB cohens kappa score: 0.453


minimum:
GB tn, fp: 285, 0
GB fn, tp: 2, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006


-----[ KNN ]-----
maximum:
KNN tn, fp: 285, 19
KNN fn, tp: 4, 7
KNN f1 score: 0.625
KNN cohens kappa score: 0.615


average:
KNN tn, fp: 279.36, 10.44
KNN fn, tp: 2.44, 4.56
KNN f1 score: 0.424
KNN cohens kappa score: 0.405


minimum:
KNN tn, fp: 271, 4
KNN fn, tp: 0, 3
KNN f1 score: 0.214
KNN cohens kappa score: 0.185