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imbalanced_classification

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metadata
library_name: tf-keras
tags:
  - tabular-classification
  - imbalanced-classification

Model Description

Keras Implementation of Imbalanced classification: credit card fraud detection

This repo contains the trained model of Imbalanced classification: credit card fraud detection. The full credit goes to: fchollet

Intended uses & limitations

  • The trained model is used to detect of a specific transaction is fraudulent or not.

Training dataset

  • Credit Card Fraud Detection
  • Due to the high imbalance of the target feature (417 frauds or 0.18% of total 284,807 samples), training weight was applied to reduce the False Negatives to the lowest level as possible.

Training procedure

Training hyperparameter

The following hyperparameters were used during training:

  • optimizer: 'Adam'
  • learning_rate: 0.01
  • loss: 'binary_crossentropy'
  • epochs: 30
  • batch_size: 2048
  • beta_1: 0.9
  • beta_2: 0.999
  • epsilon: 1e-07
  • training_precision: float32

Training Metrics

Epochs Train Loss Train Fn Train Fp Train Tn Train Tp Train Precision Train Recall Validation Loss Validation Fn Validation Fp Validation Tn Validation Tp Validation Precision Validation Recall
1 0.0 14.0 6202.0 221227.0 403.0 0.061 0.966 0.043 9.0 622.0 56264.0 66.0 0.096 0.88
2 0.0 3.0 3514.0 223915.0 414.0 0.105 0.993 0.025 10.0 528.0 56358.0 65.0 0.11 0.867
3 0.0 2.0 2419.0 225010.0 415.0 0.146 0.995 0.014 11.0 283.0 56603.0 64.0 0.184 0.853
4 0.0 3.0 2482.0 224947.0 414.0 0.143 0.993 0.027 11.0 340.0 56546.0 64.0 0.158 0.853
5 0.0 2.0 2295.0 225134.0 415.0 0.153 0.995 0.034 11.0 245.0 56641.0 64.0 0.207 0.853
6 0.0 3.0 2239.0 225190.0 414.0 0.156 0.993 0.037 10.0 495.0 56391.0 65.0 0.116 0.867
7 0.0 2.0 3095.0 224334.0 415.0 0.118 0.995 0.011 11.0 194.0 56692.0 64.0 0.248 0.853
8 0.0 4.0 1844.0 225585.0 413.0 0.183 0.99 0.035 9.0 429.0 56457.0 66.0 0.133 0.88
9 0.0 1.0 2119.0 225310.0 416.0 0.164 0.998 0.012 11.0 167.0 56719.0 64.0 0.277 0.853
10 0.0 3.0 1539.0 225890.0 414.0 0.212 0.993 0.013 13.0 144.0 56742.0 62.0 0.301 0.827
11 0.0 6.0 3444.0 223985.0 411.0 0.107 0.986 0.039 11.0 394.0 56492.0 64.0 0.14 0.853
12 0.0 4.0 3818.0 223611.0 413.0 0.098 0.99 0.03 9.0 523.0 56363.0 66.0 0.112 0.88
13 0.0 7.0 4482.0 222947.0 410.0 0.084 0.983 0.059 6.0 1364.0 55522.0 69.0 0.048 0.92
14 0.0 2.0 3064.0 224365.0 415.0 0.119 0.995 0.033 9.0 699.0 56187.0 66.0 0.086 0.88
15 0.0 4.0 3563.0 223866.0 413.0 0.104 0.99 0.066 8.0 956.0 55930.0 67.0 0.065 0.893
16 0.0 4.0 2536.0 224893.0 413.0 0.14 0.99 0.016 9.0 339.0 56547.0 66.0 0.163 0.88
17 0.0 6.0 2594.0 224835.0 411.0 0.137 0.986 0.049 8.0 821.0 56065.0 67.0 0.075 0.893
18 0.0 1.0 1911.0 225518.0 416.0 0.179 0.998 0.013 8.0 215.0 56671.0 67.0 0.238 0.893
19 0.0 2.0 1457.0 225972.0 415.0 0.222 0.995 0.018 7.0 342.0 56544.0 68.0 0.166 0.907
20 0.0 0.0 1132.0 226297.0 417.0 0.269 1.0 0.011 10.0 172.0 56714.0 65.0 0.274 0.867
21 0.0 1.0 840.0 226589.0 416.0 0.331 0.998 0.008 11.0 100.0 56786.0 64.0 0.39 0.853
22 0.0 1.0 2124.0 225305.0 416.0 0.164 0.998 0.075 10.0 350.0 56536.0 65.0 0.157 0.867
23 0.0 2.0 1457.0 225972.0 415.0 0.222 0.995 0.03 11.0 242.0 56644.0 64.0 0.209 0.853
24 0.0 5.0 2761.0 224668.0 412.0 0.13 0.988 0.297 6.0 2741.0 54145.0 69.0 0.025 0.92
25 0.0 3.0 2484.0 224945.0 414.0 0.143 0.993 0.025 10.0 199.0 56687.0 65.0 0.246 0.867
26 0.0 4.0 4867.0 222562.0 413.0 0.078 0.99 0.021 18.0 33.0 56853.0 57.0 0.633 0.76
27 0.0 8.0 4230.0 223199.0 409.0 0.088 0.981 0.053 9.0 1541.0 55345.0 66.0 0.041 0.88
28 0.0 9.0 5305.0 222124.0 408.0 0.071 0.978 0.026 9.0 398.0 56488.0 66.0 0.142 0.88
29 0.0 5.0 4846.0 222583.0 412.0 0.078 0.988 0.242 6.0 7883.0 49003.0 69.0 0.009 0.92
30 0.0 5.0 5193.0 222236.0 412.0 0.074 0.988 0.026 7.0 449.0 56437.0 68.0 0.132 0.907

Model Plot

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Model Image