ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_hypothyroid.log

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

Load 'folding_hypothyroid'
from pickle file
non empty cut in 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: 486, 117
LR fn, tp: 4, 27
LR f1 score: 0.309
LR cohens kappa score: 0.248
LR average precision score: 0.417

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

-> 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: 569, 34
KNN fn, tp: 7, 24
KNN f1 score: 0.539
KNN cohens kappa score: 0.508


------ 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: 504, 99
LR fn, tp: 4, 27
LR f1 score: 0.344
LR cohens kappa score: 0.288
LR average precision score: 0.382

-> 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: 593, 10
GB fn, tp: 4, 27
GB f1 score: 0.794
GB cohens kappa score: 0.783

-> test with 'KNN'
KNN tn, fp: 563, 40
KNN fn, tp: 5, 26
KNN f1 score: 0.536
KNN cohens kappa score: 0.503


------ 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: 498, 105
LR fn, tp: 9, 22
LR f1 score: 0.278
LR cohens kappa score: 0.217
LR average precision score: 0.236

-> 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: 599, 4
GB fn, tp: 6, 25
GB f1 score: 0.833
GB cohens kappa score: 0.825

-> test with 'KNN'
KNN tn, fp: 576, 27
KNN fn, tp: 8, 23
KNN f1 score: 0.568
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 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 472, 131
LR fn, tp: 3, 28
LR f1 score: 0.295
LR cohens kappa score: 0.232
LR average precision score: 0.253

-> test with 'RF'
RF tn, fp: 603, 0
RF fn, tp: 12, 19
RF f1 score: 0.760
RF cohens kappa score: 0.751

-> test with 'GB'
GB tn, fp: 601, 2
GB fn, tp: 9, 22
GB f1 score: 0.800
GB cohens kappa score: 0.791

-> test with 'KNN'
KNN tn, fp: 578, 25
KNN fn, tp: 7, 24
KNN f1 score: 0.600
KNN cohens kappa score: 0.575


------ 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: 530, 70
LR fn, tp: 7, 20
LR f1 score: 0.342
LR cohens kappa score: 0.295
LR average precision score: 0.348

-> 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: 597, 3
GB fn, tp: 5, 22
GB f1 score: 0.846
GB cohens kappa score: 0.840

-> test with 'KNN'
KNN tn, fp: 584, 16
KNN fn, tp: 8, 19
KNN f1 score: 0.613
KNN cohens kappa score: 0.593


====== 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 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 505, 98
LR fn, tp: 4, 27
LR f1 score: 0.346
LR cohens kappa score: 0.291
LR average precision score: 0.352

-> test with 'RF'
RF tn, fp: 598, 5
RF fn, tp: 13, 18
RF f1 score: 0.667
RF cohens kappa score: 0.652

-> test with 'GB'
GB tn, fp: 596, 7
GB fn, tp: 9, 22
GB f1 score: 0.733
GB cohens kappa score: 0.720

-> test with 'KNN'
KNN tn, fp: 574, 29
KNN fn, tp: 7, 24
KNN f1 score: 0.571
KNN cohens kappa score: 0.543


------ 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: 516, 87
LR fn, tp: 10, 21
LR f1 score: 0.302
LR cohens kappa score: 0.245
LR average precision score: 0.284

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 6, 25
RF f1 score: 0.833
RF cohens kappa score: 0.825

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 6, 25
GB f1 score: 0.806
GB cohens kappa score: 0.797

-> test with 'KNN'
KNN tn, fp: 574, 29
KNN fn, tp: 6, 25
KNN f1 score: 0.588
KNN cohens kappa score: 0.561


------ 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: 494, 109
LR fn, tp: 0, 31
LR f1 score: 0.363
LR cohens kappa score: 0.307
LR average precision score: 0.446

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 10, 21
RF f1 score: 0.778
RF cohens kappa score: 0.768

-> test with 'GB'
GB tn, fp: 600, 3
GB fn, tp: 10, 21
GB f1 score: 0.764
GB cohens kappa score: 0.753

-> test with 'KNN'
KNN tn, fp: 572, 31
KNN fn, tp: 9, 22
KNN f1 score: 0.524
KNN cohens kappa score: 0.492


------ 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: 505, 98
LR fn, tp: 7, 24
LR f1 score: 0.314
LR cohens kappa score: 0.256
LR average precision score: 0.255

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 6, 25
RF f1 score: 0.833
RF cohens kappa score: 0.825

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

-> 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 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: 484, 116
LR fn, tp: 2, 25
LR f1 score: 0.298
LR cohens kappa score: 0.243
LR average precision score: 0.441

-> test with 'RF'
RF tn, fp: 598, 2
RF fn, tp: 5, 22
RF f1 score: 0.863
RF cohens kappa score: 0.857

-> test with 'GB'
GB tn, fp: 596, 4
GB fn, tp: 3, 24
GB f1 score: 0.873
GB cohens kappa score: 0.867

-> test with 'KNN'
KNN tn, fp: 567, 33
KNN fn, tp: 4, 23
KNN f1 score: 0.554
KNN cohens kappa score: 0.527


====== 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: 511, 92
LR fn, tp: 7, 24
LR f1 score: 0.327
LR cohens kappa score: 0.270
LR average precision score: 0.340

-> 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: 603, 0
GB fn, tp: 8, 23
GB f1 score: 0.852
GB cohens kappa score: 0.845

-> test with 'KNN'
KNN tn, fp: 585, 18
KNN fn, tp: 12, 19
KNN f1 score: 0.559
KNN cohens kappa score: 0.534


------ 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: 513, 90
LR fn, tp: 7, 24
LR f1 score: 0.331
LR cohens kappa score: 0.275
LR average precision score: 0.214

-> 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: 594, 9
GB fn, tp: 7, 24
GB f1 score: 0.750
GB cohens kappa score: 0.737

-> test with 'KNN'
KNN tn, fp: 548, 55
KNN fn, tp: 5, 26
KNN f1 score: 0.464
KNN cohens kappa score: 0.424


------ 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: 496, 107
LR fn, tp: 4, 27
LR f1 score: 0.327
LR cohens kappa score: 0.269
LR average precision score: 0.435

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

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 5, 26
GB f1 score: 0.839
GB cohens kappa score: 0.830

-> test with 'KNN'
KNN tn, fp: 569, 34
KNN fn, tp: 6, 25
KNN f1 score: 0.556
KNN cohens kappa score: 0.525


------ Step 3/5: 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: 495, 108
LR fn, tp: 2, 29
LR f1 score: 0.345
LR cohens kappa score: 0.288
LR average precision score: 0.395

-> test with 'RF'
RF tn, fp: 596, 7
RF fn, tp: 11, 20
RF f1 score: 0.690
RF cohens kappa score: 0.675

-> test with 'GB'
GB tn, fp: 595, 8
GB fn, tp: 9, 22
GB f1 score: 0.721
GB cohens kappa score: 0.707

-> test with 'KNN'
KNN tn, fp: 567, 36
KNN fn, tp: 5, 26
KNN f1 score: 0.559
KNN cohens kappa score: 0.528


------ Step 3/5: 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: 523, 77
LR fn, tp: 12, 15
LR f1 score: 0.252
LR cohens kappa score: 0.199
LR average precision score: 0.191

-> test with 'RF'
RF tn, fp: 599, 1
RF fn, tp: 8, 19
RF f1 score: 0.809
RF cohens kappa score: 0.801

-> 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: 578, 22
KNN fn, tp: 5, 22
KNN f1 score: 0.620
KNN cohens kappa score: 0.598


====== 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 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 516, 87
LR fn, tp: 7, 24
LR f1 score: 0.338
LR cohens kappa score: 0.283
LR average precision score: 0.284

-> test with 'RF'
RF tn, fp: 597, 6
RF fn, tp: 8, 23
RF f1 score: 0.767
RF cohens kappa score: 0.755

-> 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: 577, 26
KNN fn, tp: 7, 24
KNN f1 score: 0.593
KNN cohens kappa score: 0.566


------ Step 4/5: 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: 502, 101
LR fn, tp: 5, 26
LR f1 score: 0.329
LR cohens kappa score: 0.272
LR average precision score: 0.327

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

-> test with 'GB'
GB tn, fp: 597, 6
GB fn, tp: 6, 25
GB f1 score: 0.806
GB cohens kappa score: 0.797

-> test with 'KNN'
KNN tn, fp: 587, 16
KNN fn, tp: 8, 23
KNN f1 score: 0.657
KNN cohens kappa score: 0.637


------ Step 4/5: 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: 501, 102
LR fn, tp: 3, 28
LR f1 score: 0.348
LR cohens kappa score: 0.292
LR average precision score: 0.401

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 7, 24
RF f1 score: 0.857
RF cohens kappa score: 0.851

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

-> test with 'KNN'
KNN tn, fp: 570, 33
KNN fn, tp: 5, 26
KNN f1 score: 0.578
KNN cohens kappa score: 0.549


------ Step 4/5: 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: 496, 107
LR fn, tp: 4, 27
LR f1 score: 0.327
LR cohens kappa score: 0.269
LR average precision score: 0.272

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 8, 23
RF f1 score: 0.821
RF cohens kappa score: 0.813

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 4, 27
GB f1 score: 0.857
GB cohens kappa score: 0.850

-> test with 'KNN'
KNN tn, fp: 570, 33
KNN fn, tp: 9, 22
KNN f1 score: 0.512
KNN cohens kappa score: 0.479


------ Step 4/5: 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: 500, 100
LR fn, tp: 7, 20
LR f1 score: 0.272
LR cohens kappa score: 0.217
LR average precision score: 0.270

-> 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: 597, 3
GB fn, tp: 7, 20
GB f1 score: 0.800
GB cohens kappa score: 0.792

-> test with 'KNN'
KNN tn, fp: 573, 27
KNN fn, tp: 7, 20
KNN f1 score: 0.541
KNN cohens kappa score: 0.514


====== 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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 94%|█████████▎| 281/300 [08:27<00:34,  1.82s/it]
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 95%|█████████▍| 284/300 [08:33<00:29,  1.85s/it]
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 95%|█████████▌| 286/300 [08:37<00:25,  1.84s/it]
 96%|█████████▌| 287/300 [08:38<00:23,  1.84s/it]
 96%|█████████▌| 288/300 [08:40<00:21,  1.83s/it]
 96%|█████████▋| 289/300 [08:42<00:20,  1.84s/it]
 97%|█████████▋| 290/300 [08:44<00:18,  1.83s/it]
 97%|█████████▋| 291/300 [08:46<00:16,  1.84s/it]
 97%|█████████▋| 292/300 [08:48<00:14,  1.83s/it]
 98%|█████████▊| 293/300 [08:49<00:12,  1.84s/it]
 98%|█████████▊| 294/300 [08:51<00:11,  1.84s/it]
 98%|█████████▊| 295/300 [08:53<00:09,  1.85s/it]
 99%|█████████▊| 296/300 [08:55<00:07,  1.85s/it]
 99%|█████████▉| 297/300 [08:57<00:05,  1.83s/it]
 99%|█████████▉| 298/300 [08:59<00:03,  1.82s/it]
100%|█████████▉| 299/300 [09:00<00:01,  1.83s/it]
100%|██████████| 300/300 [09:02<00:00,  1.83s/it]
100%|██████████| 300/300 [09:02<00:00,  1.81s/it]
-> create 2289 synthetic samples
-> test with 'LR'
LR tn, fp: 503, 100
LR fn, tp: 5, 26
LR f1 score: 0.331
LR cohens kappa score: 0.274
LR average precision score: 0.279

-> test with 'RF'
RF tn, fp: 599, 4
RF fn, tp: 10, 21
RF f1 score: 0.750
RF cohens kappa score: 0.739

-> test with 'GB'
GB tn, fp: 599, 4
GB fn, tp: 6, 25
GB f1 score: 0.833
GB cohens kappa score: 0.825

-> test with 'KNN'
KNN tn, fp: 581, 22
KNN fn, tp: 9, 22
KNN f1 score: 0.587
KNN cohens kappa score: 0.562


------ Step 5/5: 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: 489, 114
LR fn, tp: 3, 28
LR f1 score: 0.324
LR cohens kappa score: 0.265
LR average precision score: 0.373

-> test with 'RF'
RF tn, fp: 602, 1
RF fn, tp: 10, 21
RF f1 score: 0.792
RF cohens kappa score: 0.784

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

-> test with 'KNN'
KNN tn, fp: 574, 29
KNN fn, tp: 9, 22
KNN f1 score: 0.537
KNN cohens kappa score: 0.507


------ Step 5/5: 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: 470, 133
LR fn, tp: 2, 29
LR f1 score: 0.301
LR cohens kappa score: 0.238
LR average precision score: 0.405

-> test with 'RF'
RF tn, fp: 601, 2
RF fn, tp: 15, 16
RF f1 score: 0.653
RF cohens kappa score: 0.640

-> test with 'GB'
GB tn, fp: 598, 5
GB fn, tp: 11, 20
GB f1 score: 0.714
GB cohens kappa score: 0.701

-> test with 'KNN'
KNN tn, fp: 561, 42
KNN fn, tp: 6, 25
KNN f1 score: 0.510
KNN cohens kappa score: 0.475


------ Step 5/5: 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: 490, 113
LR fn, tp: 2, 29
LR f1 score: 0.335
LR cohens kappa score: 0.277
LR average precision score: 0.445

-> 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: 598, 5
GB fn, tp: 5, 26
GB f1 score: 0.839
GB cohens kappa score: 0.830

-> test with 'KNN'
KNN tn, fp: 572, 31
KNN fn, tp: 7, 24
KNN f1 score: 0.558
KNN cohens kappa score: 0.529


------ Step 5/5: 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: 485, 115
LR fn, tp: 7, 20
LR f1 score: 0.247
LR cohens kappa score: 0.189
LR average precision score: 0.179

-> test with 'RF'
RF tn, fp: 594, 6
RF fn, tp: 10, 17
RF f1 score: 0.680
RF cohens kappa score: 0.667

-> test with 'GB'
GB tn, fp: 595, 5
GB fn, tp: 8, 19
GB f1 score: 0.745
GB cohens kappa score: 0.734

-> test with 'KNN'
KNN tn, fp: 576, 24
KNN fn, tp: 6, 21
KNN f1 score: 0.583
KNN cohens kappa score: 0.560

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 530, 133
LR fn, tp: 12, 31
LR f1 score: 0.363
LR cohens kappa score: 0.307
LR average precision score: 0.446


average:
LR tn, fp: 499.36, 103.04
LR fn, tp: 5.08, 25.12
LR f1 score: 0.317
LR cohens kappa score: 0.260
LR average precision score: 0.329


minimum:
LR tn, fp: 470, 70
LR fn, tp: 0, 15
LR f1 score: 0.247
LR cohens kappa score: 0.189
LR average precision score: 0.179


-----[ RF ]-----
maximum:
RF tn, fp: 603, 8
RF fn, tp: 15, 25
RF f1 score: 0.863
RF cohens kappa score: 0.857


average:
RF tn, fp: 599.08, 3.32
RF fn, tp: 8.84, 21.36
RF f1 score: 0.777
RF cohens kappa score: 0.767


minimum:
RF tn, fp: 594, 0
RF fn, tp: 5, 16
RF f1 score: 0.653
RF cohens kappa score: 0.640


-----[ GB ]-----
maximum:
GB tn, fp: 603, 10
GB fn, tp: 11, 27
GB f1 score: 0.873
GB cohens kappa score: 0.867


average:
GB tn, fp: 597.8, 4.6
GB fn, tp: 6.52, 23.68
GB f1 score: 0.810
GB cohens kappa score: 0.801


minimum:
GB tn, fp: 593, 0
GB fn, tp: 3, 19
GB f1 score: 0.714
GB cohens kappa score: 0.701


-----[ KNN ]-----
maximum:
KNN tn, fp: 587, 55
KNN fn, tp: 12, 26
KNN f1 score: 0.657
KNN cohens kappa score: 0.637


average:
KNN tn, fp: 573.2, 29.2
KNN fn, tp: 7.04, 23.16
KNN f1 score: 0.565
KNN cohens kappa score: 0.537


minimum:
KNN tn, fp: 548, 16
KNN fn, tp: 4, 19
KNN f1 score: 0.464
KNN cohens kappa score: 0.424

  wall time: 03:49:10s, process time: 1 days 05:30:22s