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

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

Load '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: 182, 128
LR fn, tp: 2, 9
LR f1 score: 0.122
LR cohens kappa score: 0.062
LR average precision score: 0.133

-> test with 'RF'
RF tn, fp: 283, 27
RF fn, tp: 7, 4
RF f1 score: 0.190
RF cohens kappa score: 0.147

-> test with 'GB'
GB tn, fp: 287, 23
GB fn, tp: 10, 1
GB f1 score: 0.057
GB cohens kappa score: 0.011

-> test with 'KNN'
KNN tn, fp: 266, 44
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.058


------ 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: 179, 131
LR fn, tp: 3, 8
LR f1 score: 0.107
LR cohens kappa score: 0.046
LR average precision score: 0.103

-> test with 'RF'
RF tn, fp: 290, 20
RF fn, tp: 8, 3
RF f1 score: 0.176
RF cohens kappa score: 0.136

-> test with 'GB'
GB tn, fp: 287, 23
GB fn, tp: 10, 1
GB f1 score: 0.057
GB cohens kappa score: 0.011

-> test with 'KNN'
KNN tn, fp: 256, 54
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.060


------ Step 1/5: 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: 167, 143
LR fn, tp: 2, 9
LR f1 score: 0.110
LR cohens kappa score: 0.050
LR average precision score: 0.100

-> test with 'RF'
RF tn, fp: 283, 27
RF fn, tp: 8, 3
RF f1 score: 0.146
RF cohens kappa score: 0.101

-> test with 'GB'
GB tn, fp: 288, 22
GB fn, tp: 8, 3
GB f1 score: 0.167
GB cohens kappa score: 0.125

-> test with 'KNN'
KNN tn, fp: 260, 50
KNN fn, tp: 10, 1
KNN f1 score: 0.032
KNN cohens kappa score: -0.026


------ 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: 212, 98
LR fn, tp: 6, 5
LR f1 score: 0.088
LR cohens kappa score: 0.027
LR average precision score: 0.133

-> test with 'RF'
RF tn, fp: 281, 29
RF fn, tp: 7, 4
RF f1 score: 0.182
RF cohens kappa score: 0.137

-> test with 'GB'
GB tn, fp: 278, 32
GB fn, tp: 9, 2
GB f1 score: 0.089
GB cohens kappa score: 0.039

-> test with 'KNN'
KNN tn, fp: 245, 65
KNN fn, tp: 7, 4
KNN f1 score: 0.100
KNN cohens kappa score: 0.043


------ 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: 212, 94
LR fn, tp: 4, 5
LR f1 score: 0.093
LR cohens kappa score: 0.042
LR average precision score: 0.185

-> test with 'RF'
RF tn, fp: 287, 19
RF fn, tp: 5, 4
RF f1 score: 0.250
RF cohens kappa score: 0.218

-> test with 'GB'
GB tn, fp: 280, 26
GB fn, tp: 7, 2
GB f1 score: 0.108
GB cohens kappa score: 0.068

-> test with 'KNN'
KNN tn, fp: 267, 39
KNN fn, tp: 8, 1
KNN f1 score: 0.041
KNN cohens kappa score: -0.006


====== 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: 175, 135
LR fn, tp: 1, 10
LR f1 score: 0.128
LR cohens kappa score: 0.069
LR average precision score: 0.150

-> test with 'RF'
RF tn, fp: 280, 30
RF fn, tp: 6, 5
RF f1 score: 0.217
RF cohens kappa score: 0.174

-> test with 'GB'
GB tn, fp: 284, 26
GB fn, tp: 6, 5
GB f1 score: 0.238
GB cohens kappa score: 0.197

-> test with 'KNN'
KNN tn, fp: 248, 62
KNN fn, tp: 7, 4
KNN f1 score: 0.104
KNN cohens kappa score: 0.048


------ 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: 192, 118
LR fn, tp: 4, 7
LR f1 score: 0.103
LR cohens kappa score: 0.043
LR average precision score: 0.093

-> test with 'RF'
RF tn, fp: 291, 19
RF fn, tp: 8, 3
RF f1 score: 0.182
RF cohens kappa score: 0.143

-> test with 'GB'
GB tn, fp: 298, 12
GB fn, tp: 10, 1
GB f1 score: 0.083
GB cohens kappa score: 0.048

-> test with 'KNN'
KNN tn, fp: 277, 33
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.054


------ 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: 167, 143
LR fn, tp: 1, 10
LR f1 score: 0.122
LR cohens kappa score: 0.062
LR average precision score: 0.152

-> test with 'RF'
RF tn, fp: 283, 27
RF fn, tp: 7, 4
RF f1 score: 0.190
RF cohens kappa score: 0.147

-> test with 'GB'
GB tn, fp: 282, 28
GB fn, tp: 8, 3
GB f1 score: 0.143
GB cohens kappa score: 0.097

-> test with 'KNN'
KNN tn, fp: 251, 59
KNN fn, tp: 8, 3
KNN f1 score: 0.082
KNN cohens kappa score: 0.025


------ 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: 196, 114
LR fn, tp: 5, 6
LR f1 score: 0.092
LR cohens kappa score: 0.031
LR average precision score: 0.262

-> test with 'RF'
RF tn, fp: 285, 25
RF fn, tp: 7, 4
RF f1 score: 0.200
RF cohens kappa score: 0.158

-> test with 'GB'
GB tn, fp: 286, 24
GB fn, tp: 8, 3
GB f1 score: 0.158
GB cohens kappa score: 0.115

-> test with 'KNN'
KNN tn, fp: 254, 56
KNN fn, tp: 11, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.061


------ 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: 196, 110
LR fn, tp: 4, 5
LR f1 score: 0.081
LR cohens kappa score: 0.029
LR average precision score: 0.051

-> test with 'RF'
RF tn, fp: 289, 17
RF fn, tp: 9, 0
RF f1 score: 0.000
RF cohens kappa score: -0.039

-> test with 'GB'
GB tn, fp: 282, 24
GB fn, tp: 9, 0
GB f1 score: 0.000
GB cohens kappa score: -0.043

-> test with 'KNN'
KNN tn, fp: 250, 56
KNN fn, tp: 6, 3
KNN f1 score: 0.088
KNN cohens kappa score: 0.041


====== 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: 212, 98
LR fn, tp: 5, 6
LR f1 score: 0.104
LR cohens kappa score: 0.045
LR average precision score: 0.121

-> test with 'RF'
RF tn, fp: 296, 14
RF fn, tp: 9, 2
RF f1 score: 0.148
RF cohens kappa score: 0.112

-> test with 'GB'
GB tn, fp: 294, 16
GB fn, tp: 8, 3
GB f1 score: 0.200
GB cohens kappa score: 0.164

-> test with 'KNN'
KNN tn, fp: 275, 35
KNN fn, tp: 8, 3
KNN f1 score: 0.122
KNN cohens kappa score: 0.073


------ 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: 200, 110
LR fn, tp: 3, 8
LR f1 score: 0.124
LR cohens kappa score: 0.065
LR average precision score: 0.234

-> test with 'RF'
RF tn, fp: 292, 18
RF fn, tp: 5, 6
RF f1 score: 0.343
RF cohens kappa score: 0.310

-> test with 'GB'
GB tn, fp: 283, 27
GB fn, tp: 6, 5
GB f1 score: 0.233
GB cohens kappa score: 0.191

-> test with 'KNN'
KNN tn, fp: 269, 41
KNN fn, tp: 10, 1
KNN f1 score: 0.038
KNN cohens kappa score: -0.018


------ 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: 176, 134
LR fn, tp: 3, 8
LR f1 score: 0.105
LR cohens kappa score: 0.044
LR average precision score: 0.082

-> test with 'RF'
RF tn, fp: 285, 25
RF fn, tp: 8, 3
RF f1 score: 0.154
RF cohens kappa score: 0.110

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 10, 1
GB f1 score: 0.061
GB cohens kappa score: 0.016

-> test with 'KNN'
KNN tn, fp: 267, 43
KNN fn, tp: 9, 2
KNN f1 score: 0.071
KNN cohens kappa score: 0.017


------ Step 3/5: 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: 178, 132
LR fn, tp: 2, 9
LR f1 score: 0.118
LR cohens kappa score: 0.059
LR average precision score: 0.098

-> test with 'RF'
RF tn, fp: 287, 23
RF fn, tp: 7, 4
RF f1 score: 0.211
RF cohens kappa score: 0.170

-> test with 'GB'
GB tn, fp: 283, 27
GB fn, tp: 9, 2
GB f1 score: 0.100
GB cohens kappa score: 0.053

-> test with 'KNN'
KNN tn, fp: 243, 67
KNN fn, tp: 8, 3
KNN f1 score: 0.074
KNN cohens kappa score: 0.016


------ Step 3/5: 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: 203, 103
LR fn, tp: 2, 7
LR f1 score: 0.118
LR cohens kappa score: 0.068
LR average precision score: 0.088

-> test with 'RF'
RF tn, fp: 286, 20
RF fn, tp: 7, 2
RF f1 score: 0.129
RF cohens kappa score: 0.092

-> test with 'GB'
GB tn, fp: 288, 18
GB fn, tp: 6, 3
GB f1 score: 0.200
GB cohens kappa score: 0.167

-> test with 'KNN'
KNN tn, fp: 256, 50
KNN fn, tp: 9, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.051


====== 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 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 150, 160
LR fn, tp: 0, 11
LR f1 score: 0.121
LR cohens kappa score: 0.060
LR average precision score: 0.310

-> test with 'RF'
RF tn, fp: 295, 15
RF fn, tp: 9, 2
RF f1 score: 0.143
RF cohens kappa score: 0.106

-> test with 'GB'
GB tn, fp: 300, 10
GB fn, tp: 6, 5
GB f1 score: 0.385
GB cohens kappa score: 0.359

-> test with 'KNN'
KNN tn, fp: 259, 51
KNN fn, tp: 9, 2
KNN f1 score: 0.062
KNN cohens kappa score: 0.006


------ Step 4/5: 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: 190, 120
LR fn, tp: 4, 7
LR f1 score: 0.101
LR cohens kappa score: 0.041
LR average precision score: 0.133

-> test with 'RF'
RF tn, fp: 290, 20
RF fn, tp: 6, 5
RF f1 score: 0.278
RF cohens kappa score: 0.242

-> test with 'GB'
GB tn, fp: 291, 19
GB fn, tp: 6, 5
GB f1 score: 0.286
GB cohens kappa score: 0.250

-> test with 'KNN'
KNN tn, fp: 256, 54
KNN fn, tp: 7, 4
KNN f1 score: 0.116
KNN cohens kappa score: 0.062


------ Step 4/5: 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: 214, 96
LR fn, tp: 5, 6
LR f1 score: 0.106
LR cohens kappa score: 0.047
LR average precision score: 0.066

-> test with 'RF'
RF tn, fp: 287, 23
RF fn, tp: 9, 2
RF f1 score: 0.111
RF cohens kappa score: 0.067

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

-> test with 'KNN'
KNN tn, fp: 269, 41
KNN fn, tp: 9, 2
KNN f1 score: 0.074
KNN cohens kappa score: 0.021


------ Step 4/5: 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: 211, 99
LR fn, tp: 2, 9
LR f1 score: 0.151
LR cohens kappa score: 0.095
LR average precision score: 0.105

-> test with 'RF'
RF tn, fp: 292, 18
RF fn, tp: 7, 4
RF f1 score: 0.242
RF cohens kappa score: 0.206

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 10, 1
GB f1 score: 0.061
GB cohens kappa score: 0.016

-> test with 'KNN'
KNN tn, fp: 269, 41
KNN fn, tp: 10, 1
KNN f1 score: 0.038
KNN cohens kappa score: -0.018


------ Step 4/5: 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: 179, 127
LR fn, tp: 4, 5
LR f1 score: 0.071
LR cohens kappa score: 0.018
LR average precision score: 0.096

-> test with 'RF'
RF tn, fp: 273, 33
RF fn, tp: 7, 2
RF f1 score: 0.091
RF cohens kappa score: 0.048

-> test with 'GB'
GB tn, fp: 275, 31
GB fn, tp: 8, 1
GB f1 score: 0.049
GB cohens kappa score: 0.004

-> test with 'KNN'
KNN tn, fp: 264, 42
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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 96%|█████████▋| 289/300 [02:05<00:04,  2.33it/s]
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100%|█████████▉| 299/300 [02:09<00:00,  2.29it/s]
100%|██████████| 300/300 [02:10<00:00,  2.29it/s]
100%|██████████| 300/300 [02:10<00:00,  2.31it/s]
-> create 1194 synthetic samples
-> test with 'LR'
LR tn, fp: 195, 115
LR fn, tp: 4, 7
LR f1 score: 0.105
LR cohens kappa score: 0.045
LR average precision score: 0.068

-> test with 'RF'
RF tn, fp: 283, 27
RF fn, tp: 10, 1
RF f1 score: 0.051
RF cohens kappa score: 0.002

-> test with 'GB'
GB tn, fp: 286, 24
GB fn, tp: 11, 0
GB f1 score: 0.000
GB cohens kappa score: -0.049

-> test with 'KNN'
KNN tn, fp: 264, 46
KNN fn, tp: 9, 2
KNN f1 score: 0.068
KNN cohens kappa score: 0.013


------ Step 5/5: 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: 186, 124
LR fn, tp: 4, 7
LR f1 score: 0.099
LR cohens kappa score: 0.038
LR average precision score: 0.103

-> test with 'RF'
RF tn, fp: 290, 20
RF fn, tp: 5, 6
RF f1 score: 0.324
RF cohens kappa score: 0.290

-> test with 'GB'
GB tn, fp: 289, 21
GB fn, tp: 7, 4
GB f1 score: 0.222
GB cohens kappa score: 0.183

-> test with 'KNN'
KNN tn, fp: 256, 54
KNN fn, tp: 5, 6
KNN f1 score: 0.169
KNN cohens kappa score: 0.118


------ Step 5/5: 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: 157, 153
LR fn, tp: 0, 11
LR f1 score: 0.126
LR cohens kappa score: 0.066
LR average precision score: 0.208

-> test with 'RF'
RF tn, fp: 273, 37
RF fn, tp: 8, 3
RF f1 score: 0.118
RF cohens kappa score: 0.068

-> test with 'GB'
GB tn, fp: 276, 34
GB fn, tp: 9, 2
GB f1 score: 0.085
GB cohens kappa score: 0.034

-> test with 'KNN'
KNN tn, fp: 260, 50
KNN fn, tp: 9, 2
KNN f1 score: 0.063
KNN cohens kappa score: 0.007


------ Step 5/5: 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: 183, 127
LR fn, tp: 4, 7
LR f1 score: 0.097
LR cohens kappa score: 0.035
LR average precision score: 0.205

-> test with 'RF'
RF tn, fp: 284, 26
RF fn, tp: 10, 1
RF f1 score: 0.053
RF cohens kappa score: 0.004

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

-> test with 'KNN'
KNN tn, fp: 269, 41
KNN fn, tp: 10, 1
KNN f1 score: 0.038
KNN cohens kappa score: -0.018


------ Step 5/5: 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: 228, 78
LR fn, tp: 3, 6
LR f1 score: 0.129
LR cohens kappa score: 0.082
LR average precision score: 0.138

-> test with 'RF'
RF tn, fp: 289, 17
RF fn, tp: 6, 3
RF f1 score: 0.207
RF cohens kappa score: 0.174

-> test with 'GB'
GB tn, fp: 286, 20
GB fn, tp: 8, 1
GB f1 score: 0.067
GB cohens kappa score: 0.028

-> test with 'KNN'
KNN tn, fp: 275, 31
KNN fn, tp: 8, 1
KNN f1 score: 0.049
KNN cohens kappa score: 0.004

### Exercise is done.

-----[ LR ]-----
maximum:
LR tn, fp: 228, 160
LR fn, tp: 6, 11
LR f1 score: 0.151
LR cohens kappa score: 0.095
LR average precision score: 0.310


average:
LR tn, fp: 189.6, 119.6
LR fn, tp: 3.08, 7.52
LR f1 score: 0.109
LR cohens kappa score: 0.051
LR average precision score: 0.137


minimum:
LR tn, fp: 150, 78
LR fn, tp: 0, 5
LR f1 score: 0.071
LR cohens kappa score: 0.018
LR average precision score: 0.051


-----[ RF ]-----
maximum:
RF tn, fp: 296, 37
RF fn, tp: 10, 6
RF f1 score: 0.343
RF cohens kappa score: 0.310


average:
RF tn, fp: 286.16, 23.04
RF fn, tp: 7.4, 3.2
RF f1 score: 0.173
RF cohens kappa score: 0.133


minimum:
RF tn, fp: 273, 14
RF fn, tp: 5, 0
RF f1 score: 0.000
RF cohens kappa score: -0.039


-----[ GB ]-----
maximum:
GB tn, fp: 300, 34
GB fn, tp: 11, 5
GB f1 score: 0.385
GB cohens kappa score: 0.359


average:
GB tn, fp: 286.12, 23.08
GB fn, tp: 8.32, 2.28
GB f1 score: 0.128
GB cohens kappa score: 0.086


minimum:
GB tn, fp: 275, 10
GB fn, tp: 6, 0
GB f1 score: 0.000
GB cohens kappa score: -0.049


-----[ KNN ]-----
maximum:
KNN tn, fp: 277, 67
KNN fn, tp: 11, 6
KNN f1 score: 0.169
KNN cohens kappa score: 0.118


average:
KNN tn, fp: 261.0, 48.2
KNN fn, tp: 8.76, 1.84
KNN f1 score: 0.057
KNN cohens kappa score: 0.003


minimum:
KNN tn, fp: 243, 31
KNN fn, tp: 5, 0
KNN f1 score: 0.000
KNN cohens kappa score: -0.061

  wall time: 00:55:15s, process time: 07:06:20s