keras-io/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 |
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