ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_hypothyroid.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_hypothyroid
///////////////////////////////////////////

Load 'data_input/folding_hypothyroid'
from pickle file
non empty cut in data_input/folding_hypothyroid! (1 points)
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 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 559, 44
LR fn, tp: 19, 12
LR f1 score: 0.276
LR cohens kappa score: 0.227
LR average precision score: 0.207

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 9, 22
RF f1 score: 0.772
RF cohens kappa score: 0.761

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 7, 24
GB f1 score: 0.814
GB cohens kappa score: 0.804

-> test with 'KNN'
KNN tn, fp: 587, 16
KNN fn, tp: 10, 21
KNN f1 score: 0.618
KNN cohens kappa score: 0.596


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 564, 39
LR fn, tp: 23, 8
LR f1 score: 0.205
LR cohens kappa score: 0.155
LR average precision score: 0.181

-> test with 'RF'
RF tn, fp: 595, 8
RF fn, tp: 10, 21
RF f1 score: 0.700
RF cohens kappa score: 0.685

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 6, 25
GB f1 score: 0.781
GB cohens kappa score: 0.770

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 6, 25
KNN f1 score: 0.633
KNN cohens kappa score: 0.610


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 528, 75
LR fn, tp: 19, 12
LR f1 score: 0.203
LR cohens kappa score: 0.141
LR average precision score: 0.136

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 5, 26
RF f1 score: 0.867
RF cohens kappa score: 0.860

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 6, 25
GB f1 score: 0.820
GB cohens kappa score: 0.811

-> test with 'KNN'
KNN tn, fp: 591, 12
KNN fn, tp: 15, 16
KNN f1 score: 0.542
KNN cohens kappa score: 0.520


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 558, 45
LR fn, tp: 22, 9
LR f1 score: 0.212
LR cohens kappa score: 0.160
LR average precision score: 0.143

-> test with 'RF'
RF tn, fp: 603, 0
RF fn, tp: 13, 18
RF f1 score: 0.735
RF cohens kappa score: 0.725

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 12, 19
GB f1 score: 0.717
GB cohens kappa score: 0.705

-> test with 'KNN'
KNN tn, fp: 592, 11
KNN fn, tp: 14, 17
KNN f1 score: 0.576
KNN cohens kappa score: 0.556


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

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-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 44
LR fn, tp: 15, 12
LR f1 score: 0.289
LR cohens kappa score: 0.245
LR average precision score: 0.208

-> test with 'RF'
RF tn, fp: 597, 3
RF fn, tp: 8, 19
RF f1 score: 0.776
RF cohens kappa score: 0.766

-> test with 'GB'
GB tn, fp: 596, 4
GB fn, tp: 4, 23
GB f1 score: 0.852
GB cohens kappa score: 0.845

-> test with 'KNN'
KNN tn, fp: 582, 18
KNN fn, tp: 10, 17
KNN f1 score: 0.548
KNN cohens kappa score: 0.525


====== 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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100%|██████████| 10/10 [00:19<00:00,  1.93s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 559, 44
LR fn, tp: 23, 8
LR f1 score: 0.193
LR cohens kappa score: 0.140
LR average precision score: 0.121

-> test with 'RF'
RF tn, fp: 597, 6
RF fn, tp: 11, 20
RF f1 score: 0.702
RF cohens kappa score: 0.688

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 10, 21
GB f1 score: 0.712
GB cohens kappa score: 0.698

-> test with 'KNN'
KNN tn, fp: 589, 14
KNN fn, tp: 15, 16
KNN f1 score: 0.525
KNN cohens kappa score: 0.501


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 570, 33
LR fn, tp: 22, 9
LR f1 score: 0.247
LR cohens kappa score: 0.202
LR average precision score: 0.185

-> test with 'RF'
RF tn, fp: 596, 7
RF fn, tp: 7, 24
RF f1 score: 0.774
RF cohens kappa score: 0.763

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 4, 27
GB f1 score: 0.844
GB cohens kappa score: 0.835

-> test with 'KNN'
KNN tn, fp: 589, 14
KNN fn, tp: 8, 23
KNN f1 score: 0.676
KNN cohens kappa score: 0.658


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 561, 42
LR fn, tp: 22, 9
LR f1 score: 0.220
LR cohens kappa score: 0.169
LR average precision score: 0.169

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 11, 20
RF f1 score: 0.755
RF cohens kappa score: 0.744

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 7, 24
GB f1 score: 0.828
GB cohens kappa score: 0.819

-> test with 'KNN'
KNN tn, fp: 593, 10
KNN fn, tp: 13, 18
KNN f1 score: 0.610
KNN cohens kappa score: 0.591


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 50
LR fn, tp: 19, 12
LR f1 score: 0.258
LR cohens kappa score: 0.206
LR average precision score: 0.237

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 6, 25
RF f1 score: 0.847
RF cohens kappa score: 0.840

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 8, 23
GB f1 score: 0.807
GB cohens kappa score: 0.798

-> test with 'KNN'
KNN tn, fp: 592, 11
KNN fn, tp: 10, 21
KNN f1 score: 0.667
KNN cohens kappa score: 0.649


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

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-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 559, 41
LR fn, tp: 24, 3
LR f1 score: 0.085
LR cohens kappa score: 0.033
LR average precision score: 0.123

-> test with 'RF'
RF tn, fp: 598, 2
RF fn, tp: 6, 21
RF f1 score: 0.840
RF cohens kappa score: 0.833

-> test with 'GB'
GB tn, fp: 597, 3
GB fn, tp: 6, 21
GB f1 score: 0.824
GB cohens kappa score: 0.816

-> test with 'KNN'
KNN tn, fp: 584, 16
KNN fn, tp: 10, 17
KNN f1 score: 0.567
KNN cohens kappa score: 0.545


====== 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 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 570, 33
LR fn, tp: 21, 10
LR f1 score: 0.270
LR cohens kappa score: 0.226
LR average precision score: 0.229

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 14, 17
RF f1 score: 0.694
RF cohens kappa score: 0.682

-> test with 'GB'
GB tn, fp: 602, 1
GB fn, tp: 8, 23
GB f1 score: 0.836
GB cohens kappa score: 0.829

-> test with 'KNN'
KNN tn, fp: 598, 5
KNN fn, tp: 15, 16
KNN f1 score: 0.615
KNN cohens kappa score: 0.600


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 594, 9
LR fn, tp: 26, 5
LR f1 score: 0.222
LR cohens kappa score: 0.198
LR average precision score: 0.269

-> test with 'RF'
RF tn, fp: 595, 8
RF fn, tp: 5, 26
RF f1 score: 0.800
RF cohens kappa score: 0.789

-> test with 'GB'
GB tn, fp: 594, 9
GB fn, tp: 4, 27
GB f1 score: 0.806
GB cohens kappa score: 0.795

-> test with 'KNN'
KNN tn, fp: 580, 23
KNN fn, tp: 14, 17
KNN f1 score: 0.479
KNN cohens kappa score: 0.448


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

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-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 570, 33
LR fn, tp: 24, 7
LR f1 score: 0.197
LR cohens kappa score: 0.150
LR average precision score: 0.173

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 8, 23
RF f1 score: 0.793
RF cohens kappa score: 0.783

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 5, 26
GB f1 score: 0.825
GB cohens kappa score: 0.816

-> test with 'KNN'
KNN tn, fp: 586, 17
KNN fn, tp: 10, 21
KNN f1 score: 0.609
KNN cohens kappa score: 0.586


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

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100%|██████████| 10/10 [00:23<00:00,  2.05s/it]
100%|██████████| 10/10 [00:23<00:00,  2.39s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 47
LR fn, tp: 18, 13
LR f1 score: 0.286
LR cohens kappa score: 0.236
LR average precision score: 0.230

-> test with 'RF'
RF tn, fp: 597, 6
RF fn, tp: 9, 22
RF f1 score: 0.746
RF cohens kappa score: 0.733

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 8, 23
GB f1 score: 0.742
GB cohens kappa score: 0.729

-> test with 'KNN'
KNN tn, fp: 589, 14
KNN fn, tp: 11, 20
KNN f1 score: 0.615
KNN cohens kappa score: 0.595


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

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100%|██████████| 10/10 [00:18<00:00,  1.92s/it]
100%|██████████| 10/10 [00:18<00:00,  1.89s/it]
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 539, 61
LR fn, tp: 20, 7
LR f1 score: 0.147
LR cohens kappa score: 0.091
LR average precision score: 0.139

-> test with 'RF'
RF tn, fp: 598, 2
RF fn, tp: 6, 21
RF f1 score: 0.840
RF cohens kappa score: 0.833

-> test with 'GB'
GB tn, fp: 598, 2
GB fn, tp: 6, 21
GB f1 score: 0.840
GB cohens kappa score: 0.833

-> test with 'KNN'
KNN tn, fp: 587, 13
KNN fn, tp: 10, 17
KNN f1 score: 0.596
KNN cohens kappa score: 0.577


====== 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 [00:18<00:00,  1.85s/it]
100%|██████████| 10/10 [00:18<00:00,  1.87s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 52
LR fn, tp: 21, 10
LR f1 score: 0.215
LR cohens kappa score: 0.160
LR average precision score: 0.136

-> test with 'RF'
RF tn, fp: 597, 6
RF fn, tp: 9, 22
RF f1 score: 0.746
RF cohens kappa score: 0.733

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 4, 27
GB f1 score: 0.831
GB cohens kappa score: 0.822

-> test with 'KNN'
KNN tn, fp: 586, 17
KNN fn, tp: 10, 21
KNN f1 score: 0.609
KNN cohens kappa score: 0.586


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

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100%|██████████| 10/10 [00:19<00:00,  1.91s/it]
100%|██████████| 10/10 [00:19<00:00,  1.92s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 562, 41
LR fn, tp: 26, 5
LR f1 score: 0.130
LR cohens kappa score: 0.076
LR average precision score: 0.113

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 9, 22
RF f1 score: 0.786
RF cohens kappa score: 0.776

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 7, 24
GB f1 score: 0.800
GB cohens kappa score: 0.790

-> test with 'KNN'
KNN tn, fp: 590, 13
KNN fn, tp: 10, 21
KNN f1 score: 0.646
KNN cohens kappa score: 0.627


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

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 10%|█         | 1/10 [00:01<00:16,  1.85s/it]
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100%|██████████| 10/10 [00:19<00:00,  1.88s/it]
100%|██████████| 10/10 [00:19<00:00,  1.93s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 545, 58
LR fn, tp: 21, 10
LR f1 score: 0.202
LR cohens kappa score: 0.145
LR average precision score: 0.150

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 8, 23
RF f1 score: 0.836
RF cohens kappa score: 0.829

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 7, 24
GB f1 score: 0.828
GB cohens kappa score: 0.819

-> test with 'KNN'
KNN tn, fp: 594, 9
KNN fn, tp: 10, 21
KNN f1 score: 0.689
KNN cohens kappa score: 0.673


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

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100%|██████████| 10/10 [00:19<00:00,  1.98s/it]
100%|██████████| 10/10 [00:19<00:00,  1.93s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 559, 44
LR fn, tp: 20, 11
LR f1 score: 0.256
LR cohens kappa score: 0.206
LR average precision score: 0.181

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 6, 25
RF f1 score: 0.862
RF cohens kappa score: 0.855

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 4, 27
GB f1 score: 0.900
GB cohens kappa score: 0.895

-> test with 'KNN'
KNN tn, fp: 590, 13
KNN fn, tp: 12, 19
KNN f1 score: 0.603
KNN cohens kappa score: 0.582


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

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100%|██████████| 10/10 [00:19<00:00,  1.92s/it]
100%|██████████| 10/10 [00:19<00:00,  1.93s/it]
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 574, 26
LR fn, tp: 20, 7
LR f1 score: 0.233
LR cohens kappa score: 0.195
LR average precision score: 0.255

-> test with 'RF'
RF tn, fp: 597, 3
RF fn, tp: 9, 18
RF f1 score: 0.750
RF cohens kappa score: 0.740

-> test with 'GB'
GB tn, fp: 597, 3
GB fn, tp: 7, 20
GB f1 score: 0.800
GB cohens kappa score: 0.792

-> test with 'KNN'
KNN tn, fp: 587, 13
KNN fn, tp: 10, 17
KNN f1 score: 0.596
KNN cohens kappa score: 0.577


====== 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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 10%|█         | 1/10 [00:01<00:16,  1.85s/it]
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100%|██████████| 10/10 [00:19<00:00,  1.92s/it]
100%|██████████| 10/10 [00:19<00:00,  1.91s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 559, 44
LR fn, tp: 19, 12
LR f1 score: 0.276
LR cohens kappa score: 0.227
LR average precision score: 0.204

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 11, 20
RF f1 score: 0.727
RF cohens kappa score: 0.715

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 5, 26
GB f1 score: 0.852
GB cohens kappa score: 0.845

-> test with 'KNN'
KNN tn, fp: 589, 14
KNN fn, tp: 9, 22
KNN f1 score: 0.657
KNN cohens kappa score: 0.638


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

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100%|██████████| 10/10 [00:19<00:00,  1.89s/it]
100%|██████████| 10/10 [00:19<00:00,  1.92s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 572, 31
LR fn, tp: 27, 4
LR f1 score: 0.121
LR cohens kappa score: 0.073
LR average precision score: 0.185

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 9, 22
RF f1 score: 0.815
RF cohens kappa score: 0.807

-> test with 'GB'
GB tn, fp: 603, 0
GB fn, tp: 6, 25
GB f1 score: 0.893
GB cohens kappa score: 0.888

-> test with 'KNN'
KNN tn, fp: 588, 15
KNN fn, tp: 14, 17
KNN f1 score: 0.540
KNN cohens kappa score: 0.516


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:16,  1.85s/it]
 20%|██        | 2/10 [00:03<00:15,  1.92s/it]
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 60%|██████    | 6/10 [00:11<00:07,  1.91s/it]
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100%|██████████| 10/10 [00:19<00:00,  2.03s/it]
100%|██████████| 10/10 [00:19<00:00,  1.95s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 559, 44
LR fn, tp: 15, 16
LR f1 score: 0.352
LR cohens kappa score: 0.307
LR average precision score: 0.214

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 11, 20
RF f1 score: 0.755
RF cohens kappa score: 0.744

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 10, 21
GB f1 score: 0.737
GB cohens kappa score: 0.725

-> test with 'KNN'
KNN tn, fp: 585, 18
KNN fn, tp: 9, 22
KNN f1 score: 0.620
KNN cohens kappa score: 0.598


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:16,  1.85s/it]
 20%|██        | 2/10 [00:03<00:15,  1.94s/it]
 30%|███       | 3/10 [00:05<00:13,  1.91s/it]
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100%|██████████| 10/10 [00:26<00:00,  4.10s/it]
100%|██████████| 10/10 [00:26<00:00,  2.64s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 578, 25
LR fn, tp: 23, 8
LR f1 score: 0.250
LR cohens kappa score: 0.210
LR average precision score: 0.207

-> test with 'RF'
RF tn, fp: 600, 3
RF fn, tp: 6, 25
RF f1 score: 0.847
RF cohens kappa score: 0.840

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 5, 26
GB f1 score: 0.825
GB cohens kappa score: 0.816

-> test with 'KNN'
KNN tn, fp: 594, 9
KNN fn, tp: 10, 21
KNN f1 score: 0.689
KNN cohens kappa score: 0.673


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

  0%|          | 0/10 [00:00<?, ?it/s]
 10%|█         | 1/10 [00:01<00:17,  1.95s/it]
 20%|██        | 2/10 [00:03<00:16,  2.00s/it]
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 60%|██████    | 6/10 [00:12<00:08,  2.01s/it]
 70%|███████   | 7/10 [00:14<00:06,  2.02s/it]
 80%|████████  | 8/10 [00:16<00:04,  2.03s/it]
 90%|█████████ | 9/10 [00:18<00:02,  2.01s/it]
100%|██████████| 10/10 [00:19<00:00,  1.97s/it]
100%|██████████| 10/10 [00:19<00:00,  2.00s/it]
-> create 2288 synthetic samples
-> test with 'LR'
LR tn, fp: 576, 24
LR fn, tp: 20, 7
LR f1 score: 0.241
LR cohens kappa score: 0.205
LR average precision score: 0.196

-> test with 'RF'
RF tn, fp: 594, 6
RF fn, tp: 9, 18
RF f1 score: 0.706
RF cohens kappa score: 0.693

-> test with 'GB'
GB tn, fp: 594, 6
GB fn, tp: 9, 18
GB f1 score: 0.706
GB cohens kappa score: 0.693

-> test with 'KNN'
KNN tn, fp: 588, 12
KNN fn, tp: 14, 13
KNN f1 score: 0.500
KNN cohens kappa score: 0.478

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 594, 75
LR fn, tp: 27, 16
LR f1 score: 0.352
LR cohens kappa score: 0.307
LR average precision score: 0.269


average:
LR tn, fp: 561.24, 41.16
LR fn, tp: 21.16, 9.04
LR f1 score: 0.223
LR cohens kappa score: 0.175
LR average precision score: 0.184


minimum:
LR tn, fp: 528, 9
LR fn, tp: 15, 3
LR f1 score: 0.085
LR cohens kappa score: 0.033
LR average precision score: 0.113


-----[ RF ]-----
maximum:
RF tn, fp: 603, 8
RF fn, tp: 14, 26
RF f1 score: 0.867
RF cohens kappa score: 0.860


average:
RF tn, fp: 598.8, 3.6
RF fn, tp: 8.6, 21.6
RF f1 score: 0.779
RF cohens kappa score: 0.769


minimum:
RF tn, fp: 594, 0
RF fn, tp: 5, 17
RF f1 score: 0.694
RF cohens kappa score: 0.682


-----[ GB ]-----
maximum:
GB tn, fp: 603, 9
GB fn, tp: 12, 27
GB f1 score: 0.900
GB cohens kappa score: 0.895


average:
GB tn, fp: 597.88, 4.52
GB fn, tp: 6.6, 23.6
GB f1 score: 0.809
GB cohens kappa score: 0.800


minimum:
GB tn, fp: 594, 0
GB fn, tp: 4, 18
GB f1 score: 0.706
GB cohens kappa score: 0.693


-----[ KNN ]-----
maximum:
KNN tn, fp: 598, 23
KNN fn, tp: 15, 25
KNN f1 score: 0.689
KNN cohens kappa score: 0.673


average:
KNN tn, fp: 588.4, 14.0
KNN fn, tp: 11.16, 19.04
KNN f1 score: 0.601
KNN cohens kappa score: 0.580


minimum:
KNN tn, fp: 580, 5
KNN fn, tp: 6, 13
KNN f1 score: 0.479
KNN cohens kappa score: 0.448