ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_kr-vs-k-zero-one_vs_draw.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_kr-vs-k-zero-one_vs_draw
///////////////////////////////////////////

Load 'data_input/folding_kr-vs-k-zero-one_vs_draw'
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 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 4
LR fn, tp: 3, 18
LR f1 score: 0.837
LR cohens kappa score: 0.831
LR average precision score: 0.864

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 5, 16
RF f1 score: 0.865
RF cohens kappa score: 0.861

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 4, 17
GB f1 score: 0.895
GB cohens kappa score: 0.891

-> test with 'KNN'
KNN tn, fp: 554, 6
KNN fn, tp: 2, 19
KNN f1 score: 0.826
KNN cohens kappa score: 0.819


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

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100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 543, 17
LR fn, tp: 2, 19
LR f1 score: 0.667
LR cohens kappa score: 0.651
LR average precision score: 0.896

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 554, 6
KNN fn, tp: 3, 18
KNN f1 score: 0.800
KNN cohens kappa score: 0.792


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

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100%|██████████| 10/10 [00:05<00:00,  1.90it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 4
LR fn, tp: 3, 18
LR f1 score: 0.837
LR cohens kappa score: 0.831
LR average precision score: 0.922

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 3, 18
RF f1 score: 0.923
RF cohens kappa score: 0.920

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 3, 18
GB f1 score: 0.923
GB cohens kappa score: 0.920

-> test with 'KNN'
KNN tn, fp: 557, 3
KNN fn, tp: 2, 19
KNN f1 score: 0.884
KNN cohens kappa score: 0.879


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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 534, 26
LR fn, tp: 2, 19
LR f1 score: 0.576
LR cohens kappa score: 0.554
LR average precision score: 0.838

-> test with 'RF'
RF tn, fp: 554, 6
RF fn, tp: 1, 20
RF f1 score: 0.851
RF cohens kappa score: 0.845

-> test with 'GB'
GB tn, fp: 552, 8
GB fn, tp: 1, 20
GB f1 score: 0.816
GB cohens kappa score: 0.808

-> test with 'KNN'
KNN tn, fp: 545, 15
KNN fn, tp: 2, 19
KNN f1 score: 0.691
KNN cohens kappa score: 0.676


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

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-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 0
LR fn, tp: 1, 20
LR f1 score: 0.976
LR cohens kappa score: 0.975
LR average precision score: 0.963

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 555, 1
KNN fn, tp: 2, 19
KNN f1 score: 0.927
KNN cohens kappa score: 0.924


====== 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 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 516, 44
LR fn, tp: 1, 20
LR f1 score: 0.471
LR cohens kappa score: 0.440
LR average precision score: 0.899

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 0, 21
RF f1 score: 0.977
RF cohens kappa score: 0.976

-> test with 'GB'
GB tn, fp: 559, 1
GB fn, tp: 0, 21
GB f1 score: 0.977
GB cohens kappa score: 0.976

-> test with 'KNN'
KNN tn, fp: 549, 11
KNN fn, tp: 1, 20
KNN f1 score: 0.769
KNN cohens kappa score: 0.759


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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 530, 30
LR fn, tp: 0, 21
LR f1 score: 0.583
LR cohens kappa score: 0.561
LR average precision score: 0.846

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 2, 19
RF f1 score: 0.927
RF cohens kappa score: 0.924

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 550, 10
KNN fn, tp: 1, 20
KNN f1 score: 0.784
KNN cohens kappa score: 0.775


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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 541, 19
LR fn, tp: 0, 21
LR f1 score: 0.689
LR cohens kappa score: 0.673
LR average precision score: 0.918

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 555, 5
KNN fn, tp: 2, 19
KNN f1 score: 0.844
KNN cohens kappa score: 0.838


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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 7
LR fn, tp: 4, 17
LR f1 score: 0.756
LR cohens kappa score: 0.746
LR average precision score: 0.848

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 3, 18
RF f1 score: 0.900
RF cohens kappa score: 0.896

-> test with 'GB'
GB tn, fp: 558, 2
GB fn, tp: 3, 18
GB f1 score: 0.878
GB cohens kappa score: 0.874

-> test with 'KNN'
KNN tn, fp: 552, 8
KNN fn, tp: 5, 16
KNN f1 score: 0.711
KNN cohens kappa score: 0.700


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

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-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 536, 20
LR fn, tp: 2, 19
LR f1 score: 0.633
LR cohens kappa score: 0.615
LR average precision score: 0.884

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 544, 12
KNN fn, tp: 2, 19
KNN f1 score: 0.731
KNN cohens kappa score: 0.719


====== 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 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 553, 7
LR fn, tp: 2, 19
LR f1 score: 0.809
LR cohens kappa score: 0.801
LR average precision score: 0.905

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 2, 19
RF f1 score: 0.927
RF cohens kappa score: 0.924

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 554, 6
KNN fn, tp: 3, 18
KNN f1 score: 0.800
KNN cohens kappa score: 0.792


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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 550, 10
LR fn, tp: 2, 19
LR f1 score: 0.760
LR cohens kappa score: 0.749
LR average precision score: 0.887

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 549, 11
KNN fn, tp: 5, 16
KNN f1 score: 0.667
KNN cohens kappa score: 0.653


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

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-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 528, 32
LR fn, tp: 2, 19
LR f1 score: 0.528
LR cohens kappa score: 0.502
LR average precision score: 0.738

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 3, 18
RF f1 score: 0.923
RF cohens kappa score: 0.920

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 550, 10
KNN fn, tp: 4, 17
KNN f1 score: 0.708
KNN cohens kappa score: 0.696


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

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100%|██████████| 10/10 [00:04<00:00,  2.01it/s]
100%|██████████| 10/10 [00:04<00:00,  2.00it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 549, 11
LR fn, tp: 2, 19
LR f1 score: 0.745
LR cohens kappa score: 0.734
LR average precision score: 0.910

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 3, 18
RF f1 score: 0.923
RF cohens kappa score: 0.920

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 552, 8
KNN fn, tp: 2, 19
KNN f1 score: 0.792
KNN cohens kappa score: 0.783


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

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 60%|██████    | 6/10 [00:02<00:01,  2.09it/s]
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 80%|████████  | 8/10 [00:03<00:00,  2.08it/s]
 90%|█████████ | 9/10 [00:04<00:00,  2.11it/s]
100%|██████████| 10/10 [00:04<00:00,  2.11it/s]
100%|██████████| 10/10 [00:04<00:00,  2.09it/s]
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 554, 2
LR fn, tp: 2, 19
LR f1 score: 0.905
LR cohens kappa score: 0.901
LR average precision score: 0.961

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 556, 0
KNN fn, tp: 2, 19
KNN f1 score: 0.950
KNN cohens kappa score: 0.948


====== 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 [00:04<00:00,  2.07it/s]
100%|██████████| 10/10 [00:04<00:00,  2.06it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 12
LR fn, tp: 2, 19
LR f1 score: 0.731
LR cohens kappa score: 0.719
LR average precision score: 0.918

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 0, 21
RF f1 score: 1.000
RF cohens kappa score: 1.000

-> test with 'GB'
GB tn, fp: 558, 2
GB fn, tp: 0, 21
GB f1 score: 0.955
GB cohens kappa score: 0.953

-> test with 'KNN'
KNN tn, fp: 552, 8
KNN fn, tp: 1, 20
KNN f1 score: 0.816
KNN cohens kappa score: 0.808


------ 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,  2.00it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 521, 39
LR fn, tp: 0, 21
LR f1 score: 0.519
LR cohens kappa score: 0.491
LR average precision score: 0.943

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 543, 17
KNN fn, tp: 1, 20
KNN f1 score: 0.690
KNN cohens kappa score: 0.675


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

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 90%|█████████ | 9/10 [00:04<00:00,  1.99it/s]
100%|██████████| 10/10 [00:05<00:00,  1.99it/s]
100%|██████████| 10/10 [00:05<00:00,  1.96it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 558, 2
LR fn, tp: 8, 13
LR f1 score: 0.722
LR cohens kappa score: 0.714
LR average precision score: 0.714

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 7, 14
RF f1 score: 0.800
RF cohens kappa score: 0.794

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 5, 16
GB f1 score: 0.865
GB cohens kappa score: 0.861

-> test with 'KNN'
KNN tn, fp: 556, 4
KNN fn, tp: 8, 13
KNN f1 score: 0.684
KNN cohens kappa score: 0.674


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

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100%|██████████| 10/10 [00:04<00:00,  2.09it/s]
100%|██████████| 10/10 [00:04<00:00,  2.07it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 4
LR fn, tp: 1, 20
LR f1 score: 0.889
LR cohens kappa score: 0.884
LR average precision score: 0.981

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 1, 20
RF f1 score: 0.952
RF cohens kappa score: 0.951

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 559, 1
KNN fn, tp: 2, 19
KNN f1 score: 0.927
KNN cohens kappa score: 0.924


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

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100%|██████████| 10/10 [00:04<00:00,  2.11it/s]
100%|██████████| 10/10 [00:04<00:00,  2.05it/s]
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 555, 1
LR fn, tp: 4, 17
LR f1 score: 0.872
LR cohens kappa score: 0.867
LR average precision score: 0.921

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 3, 18
RF f1 score: 0.923
RF cohens kappa score: 0.920

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 556, 0
KNN fn, tp: 5, 16
KNN f1 score: 0.865
KNN cohens kappa score: 0.860


====== 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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 60%|██████    | 6/10 [00:03<00:02,  1.96it/s]
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100%|██████████| 10/10 [00:04<00:00,  2.03it/s]
100%|██████████| 10/10 [00:04<00:00,  2.02it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 548, 12
LR fn, tp: 0, 21
LR f1 score: 0.778
LR cohens kappa score: 0.768
LR average precision score: 0.968

-> test with 'RF'
RF tn, fp: 559, 1
RF fn, tp: 2, 19
RF f1 score: 0.927
RF cohens kappa score: 0.924

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 553, 7
KNN fn, tp: 0, 21
KNN f1 score: 0.857
KNN cohens kappa score: 0.851


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

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 60%|██████    | 6/10 [00:02<00:01,  2.11it/s]
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100%|██████████| 10/10 [00:04<00:00,  2.09it/s]
100%|██████████| 10/10 [00:04<00:00,  2.09it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 540, 20
LR fn, tp: 4, 17
LR f1 score: 0.586
LR cohens kappa score: 0.566
LR average precision score: 0.843

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 4, 17
GB f1 score: 0.895
GB cohens kappa score: 0.891

-> test with 'KNN'
KNN tn, fp: 549, 11
KNN fn, tp: 5, 16
KNN f1 score: 0.667
KNN cohens kappa score: 0.653


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

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 50%|█████     | 5/10 [00:02<00:02,  2.08it/s]
 60%|██████    | 6/10 [00:02<00:01,  2.06it/s]
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 90%|█████████ | 9/10 [00:04<00:00,  1.99it/s]
100%|██████████| 10/10 [00:04<00:00,  1.98it/s]
100%|██████████| 10/10 [00:04<00:00,  2.03it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 4
LR fn, tp: 4, 17
LR f1 score: 0.810
LR cohens kappa score: 0.802
LR average precision score: 0.891

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 2, 19
RF f1 score: 0.950
RF cohens kappa score: 0.948

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 2, 19
GB f1 score: 0.950
GB cohens kappa score: 0.948

-> test with 'KNN'
KNN tn, fp: 558, 2
KNN fn, tp: 4, 17
KNN f1 score: 0.850
KNN cohens kappa score: 0.845


------ 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<00:04,  1.94it/s]
 20%|██        | 2/10 [00:01<00:04,  1.92it/s]
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 40%|████      | 4/10 [00:02<00:03,  1.98it/s]
 50%|█████     | 5/10 [00:02<00:02,  2.03it/s]
 60%|██████    | 6/10 [00:03<00:01,  2.01it/s]
 70%|███████   | 7/10 [00:03<00:01,  2.02it/s]
 80%|████████  | 8/10 [00:03<00:00,  2.03it/s]
 90%|█████████ | 9/10 [00:04<00:00,  2.03it/s]
100%|██████████| 10/10 [00:04<00:00,  2.04it/s]
100%|██████████| 10/10 [00:04<00:00,  2.01it/s]
-> create 2152 synthetic samples
-> test with 'LR'
LR tn, fp: 556, 4
LR fn, tp: 3, 18
LR f1 score: 0.837
LR cohens kappa score: 0.831
LR average precision score: 0.852

-> test with 'RF'
RF tn, fp: 560, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 560, 0
GB fn, tp: 1, 20
GB f1 score: 0.976
GB cohens kappa score: 0.975

-> test with 'KNN'
KNN tn, fp: 551, 9
KNN fn, tp: 3, 18
KNN f1 score: 0.750
KNN cohens kappa score: 0.739


------ 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<00:04,  1.94it/s]
 20%|██        | 2/10 [00:01<00:04,  1.94it/s]
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 40%|████      | 4/10 [00:02<00:03,  1.94it/s]
 50%|█████     | 5/10 [00:02<00:02,  1.94it/s]
 60%|██████    | 6/10 [00:03<00:02,  1.99it/s]
 70%|███████   | 7/10 [00:03<00:01,  2.05it/s]
 80%|████████  | 8/10 [00:03<00:00,  2.08it/s]
 90%|█████████ | 9/10 [00:04<00:00,  2.05it/s]
100%|██████████| 10/10 [00:04<00:00,  2.02it/s]
100%|██████████| 10/10 [00:04<00:00,  2.00it/s]
-> create 2156 synthetic samples
-> test with 'LR'
LR tn, fp: 551, 5
LR fn, tp: 1, 20
LR f1 score: 0.870
LR cohens kappa score: 0.864
LR average precision score: 0.950

-> test with 'RF'
RF tn, fp: 556, 0
RF fn, tp: 1, 20
RF f1 score: 0.976
RF cohens kappa score: 0.975

-> test with 'GB'
GB tn, fp: 556, 0
GB fn, tp: 0, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000

-> test with 'KNN'
KNN tn, fp: 553, 3
KNN fn, tp: 2, 19
KNN f1 score: 0.884
KNN cohens kappa score: 0.879

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 558, 44
LR fn, tp: 8, 21
LR f1 score: 0.976
LR cohens kappa score: 0.975
LR average precision score: 0.981


average:
LR tn, fp: 545.76, 13.44
LR fn, tp: 2.2, 18.8
LR f1 score: 0.735
LR cohens kappa score: 0.723
LR average precision score: 0.890


minimum:
LR tn, fp: 516, 0
LR fn, tp: 0, 13
LR f1 score: 0.471
LR cohens kappa score: 0.440
LR average precision score: 0.714


-----[ RF ]-----
maximum:
RF tn, fp: 560, 6
RF fn, tp: 7, 21
RF f1 score: 1.000
RF cohens kappa score: 1.000


average:
RF tn, fp: 558.72, 0.48
RF fn, tp: 2.0, 19.0
RF f1 score: 0.938
RF cohens kappa score: 0.936


minimum:
RF tn, fp: 554, 0
RF fn, tp: 0, 14
RF f1 score: 0.800
RF cohens kappa score: 0.794


-----[ GB ]-----
maximum:
GB tn, fp: 560, 8
GB fn, tp: 5, 21
GB f1 score: 1.000
GB cohens kappa score: 1.000


average:
GB tn, fp: 558.68, 0.52
GB fn, tp: 1.64, 19.36
GB f1 score: 0.947
GB cohens kappa score: 0.945


minimum:
GB tn, fp: 552, 0
GB fn, tp: 0, 16
GB f1 score: 0.816
GB cohens kappa score: 0.808


-----[ KNN ]-----
maximum:
KNN tn, fp: 559, 17
KNN fn, tp: 8, 21
KNN f1 score: 0.950
KNN cohens kappa score: 0.948


average:
KNN tn, fp: 552.24, 6.96
KNN fn, tp: 2.76, 18.24
KNN f1 score: 0.795
KNN cohens kappa score: 0.786


minimum:
KNN tn, fp: 543, 0
KNN fn, tp: 0, 13
KNN f1 score: 0.667
KNN cohens kappa score: 0.653