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--- |
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library_name: keras |
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tags: |
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- tabular-classification |
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- imbalanced-classification |
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--- |
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## Model Description |
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### Keras Implementation of Imbalanced classification: credit card fraud detection |
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This repo contains the trained model of [Imbalanced classification: credit card fraud detection](https://keras.io/examples/structured_data/imbalanced_classification/). |
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The full credit goes to: [fchollet](https://twitter.com/fchollet) |
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## Intended uses & limitations |
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- The trained model is used to detect of a specific transaction is fraudulent or not. |
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## Training dataset |
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- [Credit Card Fraud Detection](https://www.kaggle.com/datasets/mlg-ulb/creditcardfraud) |
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- 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. |
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## Training procedure |
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### Training hyperparameter |
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The following hyperparameters were used during training: |
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- optimizer: 'Adam' |
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- learning_rate: 0.01 |
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- loss: 'binary_crossentropy' |
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- epochs: 30 |
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- batch_size: 2048 |
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- beta_1: 0.9 |
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- beta_2: 0.999 |
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- epsilon: 1e-07 |
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- training_precision: float32 |
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## Training Metrics |
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| 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 | |
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|--- |--- |--- |--- |--- |--- |--- |--- |--- |--- |--- |--- |--- |--- |--- | |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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| |
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| 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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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
