ConvGeNBenchmarkResults/data_result/CTAB-GAN/folding_winequality-red-4.log

///////////////////////////////////////////
// Running CTAB-GAN on folding_winequality-red-4
///////////////////////////////////////////

Load 'data_input/folding_winequality-red-4'
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 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 223, 87
LR fn, tp: 2, 9
LR f1 score: 0.168
LR cohens kappa score: 0.114
LR average precision score: 0.088

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 10, 1
GB f1 score: 0.118
GB cohens kappa score: 0.096

-> test with 'KNN'
KNN tn, fp: 278, 32
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.054


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

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 277, 33
LR fn, tp: 6, 5
LR f1 score: 0.204
LR cohens kappa score: 0.159
LR average precision score: 0.112

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 306, 4
GB fn, tp: 10, 1
GB f1 score: 0.125
GB cohens kappa score: 0.106

-> test with 'KNN'
KNN tn, fp: 272, 38
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.056


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

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100%|██████████| 10/10 [00:13<00:00,  1.35s/it]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 287, 23
LR fn, tp: 6, 5
LR f1 score: 0.256
LR cohens kappa score: 0.218
LR average precision score: 0.296

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 304, 6
GB fn, tp: 9, 2
GB f1 score: 0.211
GB cohens kappa score: 0.187

-> test with 'KNN'
KNN tn, fp: 280, 30
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.053


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

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 41
LR fn, tp: 9, 2
LR f1 score: 0.074
LR cohens kappa score: 0.021
LR average precision score: 0.059

-> test with 'RF'
RF tn, fp: 307, 3
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.015

-> test with 'GB'
GB tn, fp: 304, 6
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.025

-> test with 'KNN'
KNN tn, fp: 271, 39
KNN fn, tp: 9, 2
KNN f1 score: 0.077
KNN cohens kappa score: 0.024


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

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-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 291, 15
LR fn, tp: 6, 3
LR f1 score: 0.222
LR cohens kappa score: 0.191
LR average precision score: 0.157

-> test with 'RF'
RF tn, fp: 305, 1
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 304, 2
GB fn, tp: 8, 1
GB f1 score: 0.167
GB cohens kappa score: 0.155

-> test with 'KNN'
KNN tn, fp: 277, 29
KNN fn, tp: 7, 2
KNN f1 score: 0.100
KNN cohens kappa score: 0.058


====== 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 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 24
LR fn, tp: 8, 3
LR f1 score: 0.158
LR cohens kappa score: 0.115
LR average precision score: 0.129

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 309, 1
GB fn, tp: 10, 1
GB f1 score: 0.154
GB cohens kappa score: 0.145

-> test with 'KNN'
KNN tn, fp: 288, 22
KNN fn, tp: 9, 2
KNN f1 score: 0.114
KNN cohens kappa score: 0.071


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

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 247, 63
LR fn, tp: 6, 5
LR f1 score: 0.127
LR cohens kappa score: 0.072
LR average precision score: 0.073

-> test with 'RF'
RF tn, fp: 307, 3
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.015

-> test with 'GB'
GB tn, fp: 303, 7
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.027

-> test with 'KNN'
KNN tn, fp: 286, 24
KNN fn, tp: 9, 2
KNN f1 score: 0.108
KNN cohens kappa score: 0.063


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

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 258, 52
LR fn, tp: 7, 4
LR f1 score: 0.119
LR cohens kappa score: 0.066
LR average precision score: 0.092

-> test with 'RF'
RF tn, fp: 306, 4
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.019

-> test with 'GB'
GB tn, fp: 302, 8
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.030

-> test with 'KNN'
KNN tn, fp: 267, 43
KNN fn, tp: 10, 1
KNN f1 score: 0.036
KNN cohens kappa score: -0.020


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

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 228, 82
LR fn, tp: 5, 6
LR f1 score: 0.121
LR cohens kappa score: 0.064
LR average precision score: 0.202

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 306, 4
GB fn, tp: 10, 1
GB f1 score: 0.125
GB cohens kappa score: 0.106

-> test with 'KNN'
KNN tn, fp: 273, 37
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.056


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

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-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 248, 58
LR fn, tp: 6, 3
LR f1 score: 0.086
LR cohens kappa score: 0.038
LR average precision score: 0.042

-> test with 'RF'
RF tn, fp: 305, 1
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 297, 9
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.029

-> test with 'KNN'
KNN tn, fp: 269, 37
KNN fn, tp: 8, 1
KNN f1 score: 0.043
KNN cohens kappa score: -0.004


====== 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 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 291, 19
LR fn, tp: 7, 4
LR f1 score: 0.235
LR cohens kappa score: 0.198
LR average precision score: 0.188

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 286, 24
KNN fn, tp: 10, 1
KNN f1 score: 0.056
KNN cohens kappa score: 0.008


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

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 286, 24
LR fn, tp: 7, 4
LR f1 score: 0.205
LR cohens kappa score: 0.164
LR average precision score: 0.202

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 9, 2
GB f1 score: 0.222
GB cohens kappa score: 0.201

-> test with 'KNN'
KNN tn, fp: 279, 31
KNN fn, tp: 10, 1
KNN f1 score: 0.047
KNN cohens kappa score: -0.005


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

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-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 293, 17
LR fn, tp: 11, 0
LR f1 score: 0.000
LR cohens kappa score: -0.043
LR average precision score: 0.054

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.011

-> test with 'KNN'
KNN tn, fp: 271, 39
KNN fn, tp: 9, 2
KNN f1 score: 0.077
KNN cohens kappa score: 0.024


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

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100%|██████████| 10/10 [00:16<00:00,  1.32s/it]
100%|██████████| 10/10 [00:16<00:00,  1.63s/it]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 254, 56
LR fn, tp: 6, 5
LR f1 score: 0.139
LR cohens kappa score: 0.086
LR average precision score: 0.076

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 10, 1
GB f1 score: 0.143
GB cohens kappa score: 0.130

-> test with 'KNN'
KNN tn, fp: 281, 29
KNN fn, tp: 10, 1
KNN f1 score: 0.049
KNN cohens kappa score: -0.001


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

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100%|██████████| 10/10 [00:12<00:00,  1.18s/it]
100%|██████████| 10/10 [00:12<00:00,  1.20s/it]
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 294, 12
LR fn, tp: 8, 1
LR f1 score: 0.091
LR cohens kappa score: 0.059
LR average precision score: 0.084

-> test with 'RF'
RF tn, fp: 306, 0
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 305, 1
GB fn, tp: 8, 1
GB f1 score: 0.182
GB cohens kappa score: 0.173

-> test with 'KNN'
KNN tn, fp: 286, 20
KNN fn, tp: 6, 3
KNN f1 score: 0.188
KNN cohens kappa score: 0.153


====== 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 [00:21<00:00,  1.39s/it]
100%|██████████| 10/10 [00:21<00:00,  2.20s/it]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 263, 47
LR fn, tp: 5, 6
LR f1 score: 0.188
LR cohens kappa score: 0.139
LR average precision score: 0.292

-> test with 'RF'
RF tn, fp: 308, 2
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.011

-> test with 'GB'
GB tn, fp: 309, 1
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.006

-> test with 'KNN'
KNN tn, fp: 269, 41
KNN fn, tp: 8, 3
KNN f1 score: 0.109
KNN cohens kappa score: 0.057


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

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100%|██████████| 10/10 [00:13<00:00,  1.37s/it]
100%|██████████| 10/10 [00:13<00:00,  1.36s/it]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 69
LR fn, tp: 6, 5
LR f1 score: 0.118
LR cohens kappa score: 0.062
LR average precision score: 0.083

-> test with 'RF'
RF tn, fp: 304, 6
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.025

-> test with 'GB'
GB tn, fp: 301, 9
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.032

-> test with 'KNN'
KNN tn, fp: 280, 30
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.053


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

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100%|██████████| 10/10 [00:10<00:00,  1.16s/it]
100%|██████████| 10/10 [00:10<00:00,  1.10s/it]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 249, 61
LR fn, tp: 7, 4
LR f1 score: 0.105
LR cohens kappa score: 0.050
LR average precision score: 0.053

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 272, 38
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.056


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

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100%|██████████| 10/10 [00:09<00:00,  1.06it/s]
100%|██████████| 10/10 [00:09<00:00,  1.08it/s]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 227, 83
LR fn, tp: 2, 9
LR f1 score: 0.175
LR cohens kappa score: 0.121
LR average precision score: 0.087

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 308, 2
GB fn, tp: 9, 2
GB f1 score: 0.267
GB cohens kappa score: 0.253

-> test with 'KNN'
KNN tn, fp: 272, 38
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.056


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

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100%|██████████| 10/10 [00:08<00:00,  1.17it/s]
100%|██████████| 10/10 [00:08<00:00,  1.14it/s]
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 239, 67
LR fn, tp: 7, 2
LR f1 score: 0.051
LR cohens kappa score: 0.001
LR average precision score: 0.029

-> test with 'RF'
RF tn, fp: 305, 1
RF fn, tp: 8, 1
RF f1 score: 0.182
RF cohens kappa score: 0.173

-> test with 'GB'
GB tn, fp: 300, 6
GB fn, tp: 8, 1
GB f1 score: 0.125
GB cohens kappa score: 0.103

-> test with 'KNN'
KNN tn, fp: 265, 41
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.049


====== 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 [00:10<00:00,  1.02s/it]
100%|██████████| 10/10 [00:10<00:00,  1.06s/it]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 213, 97
LR fn, tp: 6, 5
LR f1 score: 0.088
LR cohens kappa score: 0.028
LR average precision score: 0.076

-> test with 'RF'
RF tn, fp: 309, 1
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.006

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.022

-> test with 'KNN'
KNN tn, fp: 274, 36
KNN fn, tp: 9, 2
KNN f1 score: 0.082
KNN cohens kappa score: 0.030


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

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 10%|█         | 1/10 [00:01<00:09,  1.08s/it]
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 60%|██████    | 6/10 [00:06<00:04,  1.02s/it]
 70%|███████   | 7/10 [00:07<00:03,  1.06s/it]
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 90%|█████████ | 9/10 [00:09<00:01,  1.00s/it]
100%|██████████| 10/10 [00:10<00:00,  1.01it/s]
100%|██████████| 10/10 [00:10<00:00,  1.02s/it]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 240, 70
LR fn, tp: 7, 4
LR f1 score: 0.094
LR cohens kappa score: 0.037
LR average precision score: 0.062

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 10, 1
GB f1 score: 0.133
GB cohens kappa score: 0.117

-> test with 'KNN'
KNN tn, fp: 279, 31
KNN fn, tp: 10, 1
KNN f1 score: 0.047
KNN cohens kappa score: -0.005


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

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 10%|█         | 1/10 [00:01<00:09,  1.07s/it]
 20%|██        | 2/10 [00:02<00:08,  1.01s/it]
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 50%|█████     | 5/10 [00:04<00:04,  1.07it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.05it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.04it/s]
 80%|████████  | 8/10 [00:08<00:02,  1.05s/it]
 90%|█████████ | 9/10 [00:09<00:01,  1.08s/it]
100%|██████████| 10/10 [00:10<00:00,  1.10s/it]
100%|██████████| 10/10 [00:10<00:00,  1.04s/it]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 300, 10
LR fn, tp: 7, 4
LR f1 score: 0.320
LR cohens kappa score: 0.293
LR average precision score: 0.250

-> test with 'RF'
RF tn, fp: 310, 0
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: 0.000

-> test with 'GB'
GB tn, fp: 307, 3
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.015

-> test with 'KNN'
KNN tn, fp: 289, 21
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.047


------ 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<00:08,  1.05it/s]
 20%|██        | 2/10 [00:01<00:07,  1.04it/s]
 30%|███       | 3/10 [00:02<00:07,  1.00s/it]
 40%|████      | 4/10 [00:03<00:05,  1.02it/s]
 50%|█████     | 5/10 [00:04<00:04,  1.06it/s]
 60%|██████    | 6/10 [00:05<00:03,  1.10it/s]
 70%|███████   | 7/10 [00:06<00:02,  1.06it/s]
 80%|████████  | 8/10 [00:07<00:01,  1.03it/s]
 90%|█████████ | 9/10 [00:08<00:00,  1.00it/s]
100%|██████████| 10/10 [00:09<00:00,  1.04s/it]
100%|██████████| 10/10 [00:09<00:00,  1.01it/s]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 241, 69
LR fn, tp: 5, 6
LR f1 score: 0.140
LR cohens kappa score: 0.085
LR average precision score: 0.184

-> test with 'RF'
RF tn, fp: 307, 3
RF fn, tp: 11, 0
RF f1 score: 0.000
RF cohens kappa score: -0.015

-> test with 'GB'
GB tn, fp: 305, 5
GB fn, tp: 10, 1
GB f1 score: 0.118
GB cohens kappa score: 0.096

-> test with 'KNN'
KNN tn, fp: 282, 28
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.052


------ 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<00:10,  1.21s/it]
 20%|██        | 2/10 [00:02<00:08,  1.09s/it]
 30%|███       | 3/10 [00:03<00:07,  1.02s/it]
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 50%|█████     | 5/10 [00:05<00:05,  1.02s/it]
 60%|██████    | 6/10 [00:06<00:03,  1.00it/s]
 70%|███████   | 7/10 [00:07<00:02,  1.01it/s]
 80%|████████  | 8/10 [00:08<00:01,  1.04it/s]
 90%|█████████ | 9/10 [00:09<00:00,  1.04it/s]
100%|██████████| 10/10 [00:10<00:00,  1.01it/s]
100%|██████████| 10/10 [00:10<00:00,  1.01s/it]
-> create 1196 synthetic samples
-> test with 'LR'
LR tn, fp: 269, 37
LR fn, tp: 6, 3
LR f1 score: 0.122
LR cohens kappa score: 0.080
LR average precision score: 0.095

-> test with 'RF'
RF tn, fp: 302, 4
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.018

-> test with 'GB'
GB tn, fp: 301, 5
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.021

-> test with 'KNN'
KNN tn, fp: 272, 34
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.047

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 300, 97
LR fn, tp: 11, 9
LR f1 score: 0.320
LR cohens kappa score: 0.293
LR average precision score: 0.296


average:
LR tn, fp: 260.56, 48.64
LR fn, tp: 6.32, 4.28
LR f1 score: 0.144
LR cohens kappa score: 0.097
LR average precision score: 0.123


minimum:
LR tn, fp: 213, 10
LR fn, tp: 2, 0
LR f1 score: 0.000
LR cohens kappa score: -0.043
LR average precision score: 0.029


-----[ RF ]-----
maximum:
RF tn, fp: 310, 6
RF fn, tp: 11, 1
RF f1 score: 0.182
RF cohens kappa score: 0.173


average:
RF tn, fp: 307.72, 1.48
RF fn, tp: 10.56, 0.04
RF f1 score: 0.007
RF cohens kappa score: -0.000


minimum:
RF tn, fp: 302, 0
RF fn, tp: 8, 0
RF f1 score: 0.000
RF cohens kappa score: -0.025


-----[ GB ]-----
maximum:
GB tn, fp: 309, 9
GB fn, tp: 11, 2
GB f1 score: 0.267
GB cohens kappa score: 0.253


average:
GB tn, fp: 304.96, 4.24
GB fn, tp: 9.96, 0.64
GB f1 score: 0.084
GB cohens kappa score: 0.065


minimum:
GB tn, fp: 297, 1
GB fn, tp: 8, 0
GB f1 score: 0.000
GB cohens kappa score: -0.032


-----[ KNN ]-----
maximum:
KNN tn, fp: 289, 43
KNN fn, tp: 11, 3
KNN f1 score: 0.188
KNN cohens kappa score: 0.153


average:
KNN tn, fp: 276.72, 32.48
KNN fn, tp: 9.64, 0.96
KNN f1 score: 0.045
KNN cohens kappa score: -0.005


minimum:
KNN tn, fp: 265, 20
KNN fn, tp: 6, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.056