ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_car_good.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_car_good
///////////////////////////////////////////

Load 'data_input/folding_car_good'
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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 162, 170
LR fn, tp: 4, 10
LR f1 score: 0.103
LR cohens kappa score: 0.030
LR average precision score: 0.058

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 5, 9
RF f1 score: 0.783
RF cohens kappa score: 0.775

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 6, 8
GB f1 score: 0.640
GB cohens kappa score: 0.627

-> test with 'KNN'
KNN tn, fp: 294, 38
KNN fn, tp: 0, 14
KNN f1 score: 0.424
KNN cohens kappa score: 0.385


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 177, 155
LR fn, tp: 2, 12
LR f1 score: 0.133
LR cohens kappa score: 0.063
LR average precision score: 0.064

-> test with 'RF'
RF tn, fp: 331, 1
RF fn, tp: 5, 9
RF f1 score: 0.750
RF cohens kappa score: 0.741

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 6, 8
GB f1 score: 0.696
GB cohens kappa score: 0.686

-> test with 'KNN'
KNN tn, fp: 280, 52
KNN fn, tp: 2, 12
KNN f1 score: 0.308
KNN cohens kappa score: 0.258


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 180, 152
LR fn, tp: 5, 9
LR f1 score: 0.103
LR cohens kappa score: 0.031
LR average precision score: 0.063

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 7, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 3, 11
GB f1 score: 0.846
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 296, 36
KNN fn, tp: 0, 14
KNN f1 score: 0.438
KNN cohens kappa score: 0.400


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 195, 137
LR fn, tp: 5, 9
LR f1 score: 0.112
LR cohens kappa score: 0.042
LR average precision score: 0.069

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 8, 6
RF f1 score: 0.600
RF cohens kappa score: 0.590

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 4, 10
GB f1 score: 0.833
GB cohens kappa score: 0.828

-> test with 'KNN'
KNN tn, fp: 275, 57
KNN fn, tp: 1, 13
KNN f1 score: 0.310
KNN cohens kappa score: 0.260


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 188, 143
LR fn, tp: 5, 8
LR f1 score: 0.098
LR cohens kappa score: 0.030
LR average precision score: 0.044

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 6, 7
RF f1 score: 0.700
RF cohens kappa score: 0.692

-> test with 'GB'
GB tn, fp: 328, 3
GB fn, tp: 2, 11
GB f1 score: 0.815
GB cohens kappa score: 0.807

-> test with 'KNN'
KNN tn, fp: 293, 38
KNN fn, tp: 1, 12
KNN f1 score: 0.381
KNN cohens kappa score: 0.341


====== 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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 162
LR fn, tp: 5, 9
LR f1 score: 0.097
LR cohens kappa score: 0.024
LR average precision score: 0.066

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 5, 9
RF f1 score: 0.783
RF cohens kappa score: 0.775

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 5, 9
GB f1 score: 0.750
GB cohens kappa score: 0.741

-> test with 'KNN'
KNN tn, fp: 285, 47
KNN fn, tp: 3, 11
KNN f1 score: 0.306
KNN cohens kappa score: 0.257


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 170, 162
LR fn, tp: 4, 10
LR f1 score: 0.108
LR cohens kappa score: 0.035
LR average precision score: 0.068

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 3, 11
RF f1 score: 0.880
RF cohens kappa score: 0.876

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 3, 11
GB f1 score: 0.846
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 306, 26
KNN fn, tp: 0, 14
KNN f1 score: 0.519
KNN cohens kappa score: 0.488


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 190, 142
LR fn, tp: 4, 10
LR f1 score: 0.120
LR cohens kappa score: 0.050
LR average precision score: 0.064

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 7, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 5, 9
GB f1 score: 0.783
GB cohens kappa score: 0.775

-> test with 'KNN'
KNN tn, fp: 285, 47
KNN fn, tp: 4, 10
KNN f1 score: 0.282
KNN cohens kappa score: 0.232


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 147
LR fn, tp: 6, 8
LR f1 score: 0.095
LR cohens kappa score: 0.022
LR average precision score: 0.052

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 9, 5
RF f1 score: 0.526
RF cohens kappa score: 0.516

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 4, 10
GB f1 score: 0.800
GB cohens kappa score: 0.793

-> test with 'KNN'
KNN tn, fp: 275, 57
KNN fn, tp: 0, 14
KNN f1 score: 0.329
KNN cohens kappa score: 0.281


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 196, 135
LR fn, tp: 3, 10
LR f1 score: 0.127
LR cohens kappa score: 0.061
LR average precision score: 0.079

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 6, 7
RF f1 score: 0.700
RF cohens kappa score: 0.692

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 4, 9
GB f1 score: 0.783
GB cohens kappa score: 0.775

-> test with 'KNN'
KNN tn, fp: 282, 49
KNN fn, tp: 0, 13
KNN f1 score: 0.347
KNN cohens kappa score: 0.303


====== 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 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 163, 169
LR fn, tp: 3, 11
LR f1 score: 0.113
LR cohens kappa score: 0.041
LR average precision score: 0.082

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 7, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 2, 12
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 270, 62
KNN fn, tp: 0, 14
KNN f1 score: 0.311
KNN cohens kappa score: 0.261


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 185, 147
LR fn, tp: 4, 10
LR f1 score: 0.117
LR cohens kappa score: 0.046
LR average precision score: 0.062

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 7, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 4, 10
GB f1 score: 0.769
GB cohens kappa score: 0.760

-> test with 'KNN'
KNN tn, fp: 274, 58
KNN fn, tp: 0, 14
KNN f1 score: 0.326
KNN cohens kappa score: 0.277


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

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-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 183, 149
LR fn, tp: 4, 10
LR f1 score: 0.116
LR cohens kappa score: 0.045
LR average precision score: 0.073

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 6, 8
RF f1 score: 0.727
RF cohens kappa score: 0.719

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 7, 7
GB f1 score: 0.609
GB cohens kappa score: 0.596

-> test with 'KNN'
KNN tn, fp: 284, 48
KNN fn, tp: 1, 13
KNN f1 score: 0.347
KNN cohens kappa score: 0.301


------ 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,  1.63it/s]
100%|██████████| 10/10 [00:06<00:00,  1.63it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 162, 170
LR fn, tp: 2, 12
LR f1 score: 0.122
LR cohens kappa score: 0.051
LR average precision score: 0.073

-> test with 'RF'
RF tn, fp: 331, 1
RF fn, tp: 6, 8
RF f1 score: 0.696
RF cohens kappa score: 0.686

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 6, 8
GB f1 score: 0.727
GB cohens kappa score: 0.719

-> test with 'KNN'
KNN tn, fp: 293, 39
KNN fn, tp: 0, 14
KNN f1 score: 0.418
KNN cohens kappa score: 0.378


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

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100%|██████████| 10/10 [00:06<00:00,  1.65it/s]
100%|██████████| 10/10 [00:06<00:00,  1.63it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 150
LR fn, tp: 6, 7
LR f1 score: 0.082
LR cohens kappa score: 0.013
LR average precision score: 0.078

-> test with 'RF'
RF tn, fp: 331, 0
RF fn, tp: 6, 7
RF f1 score: 0.700
RF cohens kappa score: 0.692

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 5, 8
GB f1 score: 0.727
GB cohens kappa score: 0.719

-> test with 'KNN'
KNN tn, fp: 270, 61
KNN fn, tp: 0, 13
KNN f1 score: 0.299
KNN cohens kappa score: 0.251


====== 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,  1.76it/s]
100%|██████████| 10/10 [00:05<00:00,  1.69it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 171, 161
LR fn, tp: 4, 10
LR f1 score: 0.108
LR cohens kappa score: 0.036
LR average precision score: 0.075

-> test with 'RF'
RF tn, fp: 331, 1
RF fn, tp: 5, 9
RF f1 score: 0.750
RF cohens kappa score: 0.741

-> test with 'GB'
GB tn, fp: 332, 0
GB fn, tp: 0, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 305, 27
KNN fn, tp: 0, 14
KNN f1 score: 0.509
KNN cohens kappa score: 0.478


------ 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,  1.62it/s]
100%|██████████| 10/10 [00:06<00:00,  1.61it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 175, 157
LR fn, tp: 5, 9
LR f1 score: 0.100
LR cohens kappa score: 0.027
LR average precision score: 0.054

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 11, 3
RF f1 score: 0.353
RF cohens kappa score: 0.344

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 7, 7
GB f1 score: 0.609
GB cohens kappa score: 0.596

-> test with 'KNN'
KNN tn, fp: 271, 61
KNN fn, tp: 0, 14
KNN f1 score: 0.315
KNN cohens kappa score: 0.264


------ 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,  1.60it/s]
100%|██████████| 10/10 [00:06<00:00,  1.57it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 179, 153
LR fn, tp: 5, 9
LR f1 score: 0.102
LR cohens kappa score: 0.030
LR average precision score: 0.087

-> test with 'RF'
RF tn, fp: 331, 1
RF fn, tp: 6, 8
RF f1 score: 0.696
RF cohens kappa score: 0.686

-> test with 'GB'
GB tn, fp: 330, 2
GB fn, tp: 3, 11
GB f1 score: 0.815
GB cohens kappa score: 0.807

-> test with 'KNN'
KNN tn, fp: 282, 50
KNN fn, tp: 0, 14
KNN f1 score: 0.359
KNN cohens kappa score: 0.313


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

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100%|██████████| 10/10 [00:07<00:00,  1.36it/s]
100%|██████████| 10/10 [00:07<00:00,  1.36it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 178, 154
LR fn, tp: 6, 8
LR f1 score: 0.091
LR cohens kappa score: 0.018
LR average precision score: 0.053

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 6, 8
RF f1 score: 0.727
RF cohens kappa score: 0.719

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 2, 12
GB f1 score: 0.889
GB cohens kappa score: 0.884

-> test with 'KNN'
KNN tn, fp: 299, 33
KNN fn, tp: 0, 14
KNN f1 score: 0.459
KNN cohens kappa score: 0.423


------ 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,  1.61it/s]
100%|██████████| 10/10 [00:06<00:00,  1.62it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 162, 169
LR fn, tp: 1, 12
LR f1 score: 0.124
LR cohens kappa score: 0.057
LR average precision score: 0.121

-> test with 'RF'
RF tn, fp: 330, 1
RF fn, tp: 9, 4
RF f1 score: 0.444
RF cohens kappa score: 0.433

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 5, 8
GB f1 score: 0.727
GB cohens kappa score: 0.719

-> test with 'KNN'
KNN tn, fp: 312, 19
KNN fn, tp: 3, 10
KNN f1 score: 0.476
KNN cohens kappa score: 0.447


====== 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,  1.65it/s]
100%|██████████| 10/10 [00:06<00:00,  1.60it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 168, 164
LR fn, tp: 5, 9
LR f1 score: 0.096
LR cohens kappa score: 0.023
LR average precision score: 0.055

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 10, 4
RF f1 score: 0.444
RF cohens kappa score: 0.434

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 9, 5
GB f1 score: 0.500
GB cohens kappa score: 0.488

-> test with 'KNN'
KNN tn, fp: 295, 37
KNN fn, tp: 0, 14
KNN f1 score: 0.431
KNN cohens kappa score: 0.392


------ 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,  1.71it/s]
100%|██████████| 10/10 [00:06<00:00,  1.60it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 195, 137
LR fn, tp: 6, 8
LR f1 score: 0.101
LR cohens kappa score: 0.029
LR average precision score: 0.086

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 6, 8
RF f1 score: 0.727
RF cohens kappa score: 0.719

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 7, 7
GB f1 score: 0.636
GB cohens kappa score: 0.625

-> test with 'KNN'
KNN tn, fp: 280, 52
KNN fn, tp: 0, 14
KNN f1 score: 0.350
KNN cohens kappa score: 0.303


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

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 60%|██████    | 6/10 [00:04<00:03,  1.28it/s]
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100%|██████████| 10/10 [00:09<00:00,  1.01s/it]
100%|██████████| 10/10 [00:09<00:00,  1.08it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 191, 141
LR fn, tp: 5, 9
LR f1 score: 0.110
LR cohens kappa score: 0.039
LR average precision score: 0.081

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 6, 8
RF f1 score: 0.727
RF cohens kappa score: 0.719

-> test with 'GB'
GB tn, fp: 329, 3
GB fn, tp: 3, 11
GB f1 score: 0.786
GB cohens kappa score: 0.777

-> test with 'KNN'
KNN tn, fp: 294, 38
KNN fn, tp: 4, 10
KNN f1 score: 0.323
KNN cohens kappa score: 0.277


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:00<00:07,  1.25it/s]
 20%|██        | 2/10 [00:01<00:05,  1.51it/s]
 30%|███       | 3/10 [00:01<00:04,  1.56it/s]
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 50%|█████     | 5/10 [00:03<00:03,  1.61it/s]
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100%|██████████| 10/10 [00:05<00:00,  1.78it/s]
100%|██████████| 10/10 [00:05<00:00,  1.67it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 174, 158
LR fn, tp: 2, 12
LR f1 score: 0.130
LR cohens kappa score: 0.060
LR average precision score: 0.082

-> test with 'RF'
RF tn, fp: 332, 0
RF fn, tp: 7, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 331, 1
GB fn, tp: 7, 7
GB f1 score: 0.636
GB cohens kappa score: 0.625

-> test with 'KNN'
KNN tn, fp: 291, 41
KNN fn, tp: 0, 14
KNN f1 score: 0.406
KNN cohens kappa score: 0.365


------ 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<00:06,  1.39it/s]
 20%|██        | 2/10 [00:01<00:05,  1.47it/s]
 30%|███       | 3/10 [00:01<00:04,  1.58it/s]
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 50%|█████     | 5/10 [00:03<00:03,  1.55it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.54it/s]
 70%|███████   | 7/10 [00:04<00:01,  1.55it/s]
 80%|████████  | 8/10 [00:05<00:01,  1.58it/s]
 90%|█████████ | 9/10 [00:05<00:00,  1.63it/s]
100%|██████████| 10/10 [00:06<00:00,  1.68it/s]
100%|██████████| 10/10 [00:06<00:00,  1.60it/s]
-> create 1272 synthetic samples
-> test with 'LR'
LR tn, fp: 181, 150
LR fn, tp: 5, 8
LR f1 score: 0.094
LR cohens kappa score: 0.026
LR average precision score: 0.073

-> test with 'RF'
RF tn, fp: 330, 1
RF fn, tp: 6, 7
RF f1 score: 0.667
RF cohens kappa score: 0.657

-> test with 'GB'
GB tn, fp: 330, 1
GB fn, tp: 0, 13
GB f1 score: 0.963
GB cohens kappa score: 0.961

-> test with 'KNN'
KNN tn, fp: 293, 38
KNN fn, tp: 0, 13
KNN f1 score: 0.406
KNN cohens kappa score: 0.368

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 196, 170
LR fn, tp: 6, 12
LR f1 score: 0.133
LR cohens kappa score: 0.063
LR average precision score: 0.121


average:
LR tn, fp: 178.44, 153.36
LR fn, tp: 4.24, 9.56
LR f1 score: 0.108
LR cohens kappa score: 0.037
LR average precision score: 0.071


minimum:
LR tn, fp: 162, 135
LR fn, tp: 1, 7
LR f1 score: 0.082
LR cohens kappa score: 0.013
LR average precision score: 0.044


-----[ RF ]-----
maximum:
RF tn, fp: 332, 1
RF fn, tp: 11, 11
RF f1 score: 0.880
RF cohens kappa score: 0.876


average:
RF tn, fp: 331.56, 0.24
RF fn, tp: 6.6, 7.2
RF f1 score: 0.669
RF cohens kappa score: 0.660


minimum:
RF tn, fp: 330, 0
RF fn, tp: 3, 3
RF f1 score: 0.353
RF cohens kappa score: 0.344


-----[ GB ]-----
maximum:
GB tn, fp: 332, 3
GB fn, tp: 9, 14
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 330.6, 1.2
GB fn, tp: 4.36, 9.44
GB f1 score: 0.764
GB cohens kappa score: 0.756


minimum:
GB tn, fp: 328, 0
GB fn, tp: 0, 5
GB f1 score: 0.500
GB cohens kappa score: 0.488


-----[ KNN ]-----
maximum:
KNN tn, fp: 312, 62
KNN fn, tp: 4, 14
KNN f1 score: 0.519
KNN cohens kappa score: 0.488


average:
KNN tn, fp: 287.36, 44.44
KNN fn, tp: 0.76, 13.04
KNN f1 score: 0.375
KNN cohens kappa score: 0.332


minimum:
KNN tn, fp: 270, 19
KNN fn, tp: 0, 10
KNN f1 score: 0.282
KNN cohens kappa score: 0.232