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► Code examples / Structured Data / Imbalanced classification: credit card fraud detection

Imbalanced classification: credit card fraud detection

Author: fchollet
Date created: 2019/05/28
Last modified: 2020/04/17
Description: Demonstration of how to handle highly imbalanced classification problems.

ⓘ This example uses Keras 3

View in Colab • GitHub source

Introduction

This example looks at the Kaggle Credit Card Fraud Detection dataset to demonstrate how to train a classification model on data with highly imbalanced classes.

First, vectorize the CSV data

import csv import numpy as np # Get the real data from https://www.kaggle.com/mlg-ulb/creditcardfraud/ fname = "/Users/fchollet/Downloads/creditcard.csv" all_features = [] all_targets = [] with open(fname) as f: for i, line in enumerate(f): if i == 0: print("HEADER:", line.strip()) continue # Skip header fields = line.strip().split(",") all_features.append([float(v.replace('"', "")) for v in fields[:-1]]) all_targets.append([int(fields[-1].replace('"', ""))]) if i == 1: print("EXAMPLE FEATURES:", all_features[-1]) features = np.array(all_features, dtype="float32") targets = np.array(all_targets, dtype="uint8") print("features.shape:", features.shape) print("targets.shape:", targets.shape) 
HEADER: "Time","V1","V2","V3","V4","V5","V6","V7","V8","V9","V10","V11","V12","V13","V14","V15","V16","V17","V18","V19","V20","V21","V22","V23","V24","V25","V26","V27","V28","Amount","Class" EXAMPLE FEATURES: [0.0, -1.3598071336738, -0.0727811733098497, 2.53634673796914, 1.37815522427443, -0.338320769942518, 0.462387777762292, 0.239598554061257, 0.0986979012610507, 0.363786969611213, 0.0907941719789316, -0.551599533260813, -0.617800855762348, -0.991389847235408, -0.311169353699879, 1.46817697209427, -0.470400525259478, 0.207971241929242, 0.0257905801985591, 0.403992960255733, 0.251412098239705, -0.018306777944153, 0.277837575558899, -0.110473910188767, 0.0669280749146731, 0.128539358273528, -0.189114843888824, 0.133558376740387, -0.0210530534538215, 149.62] features.shape: (284807, 30) targets.shape: (284807, 1)

Prepare a validation set

num_val_samples = int(len(features) * 0.2) train_features = features[:-num_val_samples] train_targets = targets[:-num_val_samples] val_features = features[-num_val_samples:] val_targets = targets[-num_val_samples:] print("Number of training samples:", len(train_features)) print("Number of validation samples:", len(val_features)) 
Number of training samples: 227846 Number of validation samples: 56961

Analyze class imbalance in the targets

counts = np.bincount(train_targets[:, 0]) print( "Number of positive samples in training data: {} ({:.2f}% of total)".format( counts[1], 100 * float(counts[1]) / len(train_targets) ) ) weight_for_0 = 1.0 / counts[0] weight_for_1 = 1.0 / counts[1] 
Number of positive samples in training data: 417 (0.18% of total)

Normalize the data using training set statistics

mean = np.mean(train_features, axis=0) train_features -= mean val_features -= mean std = np.std(train_features, axis=0) train_features /= std val_features /= std

Build a binary classification model

import keras model = keras.Sequential( [ keras.Input(shape=train_features.shape[1:]), keras.layers.Dense(256, activation="relu"), keras.layers.Dense(256, activation="relu"), keras.layers.Dropout(0.3), keras.layers.Dense(256, activation="relu"), keras.layers.Dropout(0.3), keras.layers.Dense(1, activation="sigmoid"), ] ) model.summary() 
Model: "sequential" 
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓ ┃ Layer (type) ┃ Output Shape ┃ Param # ┃ ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩ │ dense (Dense) │ (None, 256) │ 7,936 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dense_1 (Dense) │ (None, 256) │ 65,792 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dropout (Dropout) │ (None, 256) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dense_2 (Dense) │ (None, 256) │ 65,792 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dropout_1 (Dropout) │ (None, 256) │ 0 │ ├─────────────────────────────────┼───────────────────────────┼────────────┤ │ dense_3 (Dense) │ (None, 1) │ 257 │ └─────────────────────────────────┴───────────────────────────┴────────────┘ 
 Total params: 139,777 (546.00 KB) 
 Trainable params: 139,777 (546.00 KB) 
 Non-trainable params: 0 (0.00 B)

Train the model with class_weight argument

metrics = [ keras.metrics.FalseNegatives(name="fn"), keras.metrics.FalsePositives(name="fp"), keras.metrics.TrueNegatives(name="tn"), keras.metrics.TruePositives(name="tp"), keras.metrics.Precision(name="precision"), keras.metrics.Recall(name="recall"), ] model.compile( optimizer=keras.optimizers.Adam(1e-2), loss="binary_crossentropy", metrics=metrics ) callbacks = [keras.callbacks.ModelCheckpoint("fraud_model_at_epoch_{epoch}.keras")] class_weight = {0: weight_for_0, 1: weight_for_1} model.fit( train_features, train_targets, batch_size=2048, epochs=30, verbose=2, callbacks=callbacks, validation_data=(val_features, val_targets), class_weight=class_weight, ) 
Epoch 1/30 112/112 - 3s - 24ms/step - fn: 39.0000 - fp: 25593.0000 - loss: 2.2586e-06 - precision: 0.0146 - recall: 0.9065 - tn: 201836.0000 - tp: 378.0000 - val_fn: 5.0000 - val_fp: 3430.0000 - val_loss: 0.1872 - val_precision: 0.0200 - val_recall: 0.9333 - val_tn: 53456.0000 - val_tp: 70.0000 Epoch 2/30 112/112 - 0s - 991us/step - fn: 32.0000 - fp: 7936.0000 - loss: 1.5505e-06 - precision: 0.0463 - recall: 0.9233 - tn: 219493.0000 - tp: 385.0000 - val_fn: 7.0000 - val_fp: 2351.0000 - val_loss: 0.1930 - val_precision: 0.0281 - val_recall: 0.9067 - val_tn: 54535.0000 - val_tp: 68.0000 Epoch 3/30 112/112 - 0s - 1ms/step - fn: 31.0000 - fp: 6716.0000 - loss: 1.2987e-06 - precision: 0.0544 - recall: 0.9257 - tn: 220713.0000 - tp: 386.0000 - val_fn: 4.0000 - val_fp: 3374.0000 - val_loss: 0.1781 - val_precision: 0.0206 - val_recall: 0.9467 - val_tn: 53512.0000 - val_tp: 71.0000 Epoch 4/30 112/112 - 0s - 1ms/step - fn: 25.0000 - fp: 7348.0000 - loss: 1.1292e-06 - precision: 0.0506 - recall: 0.9400 - tn: 220081.0000 - tp: 392.0000 - val_fn: 6.0000 - val_fp: 1405.0000 - val_loss: 0.0796 - val_precision: 0.0468 - val_recall: 0.9200 - val_tn: 55481.0000 - val_tp: 69.0000 Epoch 5/30 112/112 - 0s - 926us/step - fn: 19.0000 - fp: 6720.0000 - loss: 8.0334e-07 - precision: 0.0559 - recall: 0.9544 - tn: 220709.0000 - tp: 398.0000 - val_fn: 11.0000 - val_fp: 315.0000 - val_loss: 0.0212 - val_precision: 0.1689 - val_recall: 0.8533 - val_tn: 56571.0000 - val_tp: 64.0000 Epoch 6/30 112/112 - 0s - 1ms/step - fn: 19.0000 - fp: 6706.0000 - loss: 8.6899e-07 - precision: 0.0560 - recall: 0.9544 - tn: 220723.0000 - tp: 398.0000 - val_fn: 8.0000 - val_fp: 1262.0000 - val_loss: 0.0801 - val_precision: 0.0504 - val_recall: 0.8933 - val_tn: 55624.0000 - val_tp: 67.0000 Epoch 7/30 112/112 - 0s - 1ms/step - fn: 15.0000 - fp: 5161.0000 - loss: 6.5298e-07 - precision: 0.0723 - recall: 0.9640 - tn: 222268.0000 - tp: 402.0000 - val_fn: 7.0000 - val_fp: 1157.0000 - val_loss: 0.0623 - val_precision: 0.0555 - val_recall: 0.9067 - val_tn: 55729.0000 - val_tp: 68.0000 Epoch 8/30 112/112 - 0s - 1ms/step - fn: 11.0000 - fp: 6381.0000 - loss: 6.7164e-07 - precision: 0.0598 - recall: 0.9736 - tn: 221048.0000 - tp: 406.0000 - val_fn: 10.0000 - val_fp: 346.0000 - val_loss: 0.0270 - val_precision: 0.1582 - val_recall: 0.8667 - val_tn: 56540.0000 - val_tp: 65.0000 Epoch 9/30 112/112 - 0s - 1ms/step - fn: 16.0000 - fp: 7259.0000 - loss: 8.9098e-07 - precision: 0.0523 - recall: 0.9616 - tn: 220170.0000 - tp: 401.0000 - val_fn: 7.0000 - val_fp: 1998.0000 - val_loss: 0.1073 - val_precision: 0.0329 - val_recall: 0.9067 - val_tn: 54888.0000 - val_tp: 68.0000 Epoch 10/30 112/112 - 0s - 999us/step - fn: 19.0000 - fp: 7792.0000 - loss: 9.2179e-07 - precision: 0.0486 - recall: 0.9544 - tn: 219637.0000 - tp: 398.0000 - val_fn: 7.0000 - val_fp: 1515.0000 - val_loss: 0.0800 - val_precision: 0.0430 - val_recall: 0.9067 - val_tn: 55371.0000 - val_tp: 68.0000 Epoch 11/30 112/112 - 0s - 1ms/step - fn: 13.0000 - fp: 5828.0000 - loss: 6.4193e-07 - precision: 0.0648 - recall: 0.9688 - tn: 221601.0000 - tp: 404.0000 - val_fn: 9.0000 - val_fp: 794.0000 - val_loss: 0.0410 - val_precision: 0.0767 - val_recall: 0.8800 - val_tn: 56092.0000 - val_tp: 66.0000 Epoch 12/30 112/112 - 0s - 959us/step - fn: 10.0000 - fp: 6400.0000 - loss: 7.4358e-07 - precision: 0.0598 - recall: 0.9760 - tn: 221029.0000 - tp: 407.0000 - val_fn: 8.0000 - val_fp: 593.0000 - val_loss: 0.0466 - val_precision: 0.1015 - val_recall: 0.8933 - val_tn: 56293.0000 - val_tp: 67.0000 Epoch 13/30 112/112 - 0s - 913us/step - fn: 9.0000 - fp: 5756.0000 - loss: 6.8158e-07 - precision: 0.0662 - recall: 0.9784 - tn: 221673.0000 - tp: 408.0000 - val_fn: 11.0000 - val_fp: 280.0000 - val_loss: 0.0336 - val_precision: 0.1860 - val_recall: 0.8533 - val_tn: 56606.0000 - val_tp: 64.0000 Epoch 14/30 112/112 - 0s - 960us/step - fn: 13.0000 - fp: 6699.0000 - loss: 1.0667e-06 - precision: 0.0569 - recall: 0.9688 - tn: 220730.0000 - tp: 404.0000 - val_fn: 9.0000 - val_fp: 1165.0000 - val_loss: 0.0885 - val_precision: 0.0536 - val_recall: 0.8800 - val_tn: 55721.0000 - val_tp: 66.0000 Epoch 15/30 112/112 - 0s - 1ms/step - fn: 15.0000 - fp: 6705.0000 - loss: 6.8100e-07 - precision: 0.0566 - recall: 0.9640 - tn: 220724.0000 - tp: 402.0000 - val_fn: 10.0000 - val_fp: 750.0000 - val_loss: 0.0367 - val_precision: 0.0798 - val_recall: 0.8667 - val_tn: 56136.0000 - val_tp: 65.0000 Epoch 16/30 112/112 - 0s - 1ms/step - fn: 8.0000 - fp: 4288.0000 - loss: 4.1541e-07 - precision: 0.0871 - recall: 0.9808 - tn: 223141.0000 - tp: 409.0000 - val_fn: 11.0000 - val_fp: 351.0000 - val_loss: 0.0199 - val_precision: 0.1542 - val_recall: 0.8533 - val_tn: 56535.0000 - val_tp: 64.0000 Epoch 17/30 112/112 - 0s - 949us/step - fn: 8.0000 - fp: 4598.0000 - loss: 4.3510e-07 - precision: 0.0817 - recall: 0.9808 - tn: 222831.0000 - tp: 409.0000 - val_fn: 10.0000 - val_fp: 688.0000 - val_loss: 0.0296 - val_precision: 0.0863 - val_recall: 0.8667 - val_tn: 56198.0000 - val_tp: 65.0000 Epoch 18/30 112/112 - 0s - 946us/step - fn: 7.0000 - fp: 5544.0000 - loss: 4.6239e-07 - precision: 0.0689 - recall: 0.9832 - tn: 221885.0000 - tp: 410.0000 - val_fn: 8.0000 - val_fp: 444.0000 - val_loss: 0.0260 - val_precision: 0.1311 - val_recall: 0.8933 - val_tn: 56442.0000 - val_tp: 67.0000 Epoch 19/30 112/112 - 0s - 972us/step - fn: 3.0000 - fp: 2920.0000 - loss: 2.7543e-07 - precision: 0.1242 - recall: 0.9928 - tn: 224509.0000 - tp: 414.0000 - val_fn: 9.0000 - val_fp: 510.0000 - val_loss: 0.0245 - val_precision: 0.1146 - val_recall: 0.8800 - val_tn: 56376.0000 - val_tp: 66.0000 Epoch 20/30 112/112 - 0s - 1ms/step - fn: 6.0000 - fp: 5351.0000 - loss: 5.7495e-07 - precision: 0.0713 - recall: 0.9856 - tn: 222078.0000 - tp: 411.0000 - val_fn: 9.0000 - val_fp: 547.0000 - val_loss: 0.0255 - val_precision: 0.1077 - val_recall: 0.8800 - val_tn: 56339.0000 - val_tp: 66.0000 Epoch 21/30 112/112 - 0s - 1ms/step - fn: 6.0000 - fp: 3808.0000 - loss: 5.1475e-07 - precision: 0.0974 - recall: 0.9856 - tn: 223621.0000 - tp: 411.0000 - val_fn: 10.0000 - val_fp: 624.0000 - val_loss: 0.0320 - val_precision: 0.0943 - val_recall: 0.8667 - val_tn: 56262.0000 - val_tp: 65.0000 Epoch 22/30 112/112 - 0s - 1ms/step - fn: 6.0000 - fp: 5117.0000 - loss: 5.5465e-07 - precision: 0.0743 - recall: 0.9856 - tn: 222312.0000 - tp: 411.0000 - val_fn: 10.0000 - val_fp: 836.0000 - val_loss: 0.0556 - val_precision: 0.0721 - val_recall: 0.8667 - val_tn: 56050.0000 - val_tp: 65.0000 Epoch 23/30 112/112 - 0s - 939us/step - fn: 8.0000 - fp: 5583.0000 - loss: 5.5407e-07 - precision: 0.0683 - recall: 0.9808 - tn: 221846.0000 - tp: 409.0000 - val_fn: 12.0000 - val_fp: 501.0000 - val_loss: 0.0300 - val_precision: 0.1117 - val_recall: 0.8400 - val_tn: 56385.0000 - val_tp: 63.0000 Epoch 24/30 112/112 - 0s - 958us/step - fn: 5.0000 - fp: 3933.0000 - loss: 4.7133e-07 - precision: 0.0948 - recall: 0.9880 - tn: 223496.0000 - tp: 412.0000 - val_fn: 12.0000 - val_fp: 211.0000 - val_loss: 0.0326 - val_precision: 0.2299 - val_recall: 0.8400 - val_tn: 56675.0000 - val_tp: 63.0000 Epoch 25/30 112/112 - 0s - 1ms/step - fn: 7.0000 - fp: 5695.0000 - loss: 7.1277e-07 - precision: 0.0672 - recall: 0.9832 - tn: 221734.0000 - tp: 410.0000 - val_fn: 9.0000 - val_fp: 802.0000 - val_loss: 0.0598 - val_precision: 0.0760 - val_recall: 0.8800 - val_tn: 56084.0000 - val_tp: 66.0000 Epoch 26/30 112/112 - 0s - 949us/step - fn: 5.0000 - fp: 3853.0000 - loss: 4.1797e-07 - precision: 0.0966 - recall: 0.9880 - tn: 223576.0000 - tp: 412.0000 - val_fn: 8.0000 - val_fp: 771.0000 - val_loss: 0.0409 - val_precision: 0.0800 - val_recall: 0.8933 - val_tn: 56115.0000 - val_tp: 67.0000 Epoch 27/30 112/112 - 0s - 947us/step - fn: 4.0000 - fp: 3873.0000 - loss: 3.7369e-07 - precision: 0.0964 - recall: 0.9904 - tn: 223556.0000 - tp: 413.0000 - val_fn: 6.0000 - val_fp: 2208.0000 - val_loss: 0.1370 - val_precision: 0.0303 - val_recall: 0.9200 - val_tn: 54678.0000 - val_tp: 69.0000 Epoch 28/30 112/112 - 0s - 892us/step - fn: 5.0000 - fp: 4619.0000 - loss: 4.1290e-07 - precision: 0.0819 - recall: 0.9880 - tn: 222810.0000 - tp: 412.0000 - val_fn: 8.0000 - val_fp: 551.0000 - val_loss: 0.0273 - val_precision: 0.1084 - val_recall: 0.8933 - val_tn: 56335.0000 - val_tp: 67.0000 Epoch 29/30 112/112 - 0s - 931us/step - fn: 1.0000 - fp: 3336.0000 - loss: 2.5478e-07 - precision: 0.1109 - recall: 0.9976 - tn: 224093.0000 - tp: 416.0000 - val_fn: 9.0000 - val_fp: 487.0000 - val_loss: 0.0238 - val_precision: 0.1193 - val_recall: 0.8800 - val_tn: 56399.0000 - val_tp: 66.0000 Epoch 30/30 112/112 - 0s - 1ms/step - fn: 2.0000 - fp: 3521.0000 - loss: 4.1991e-07 - precision: 0.1054 - recall: 0.9952 - tn: 223908.0000 - tp: 415.0000 - val_fn: 10.0000 - val_fp: 462.0000 - val_loss: 0.0331 - val_precision: 0.1233 - val_recall: 0.8667 - val_tn: 56424.0000 - val_tp: 65.0000 <keras.src.callbacks.history.History at 0x7f22b41f3430>

Conclusions

At the end of training, out of 56,961 validation transactions, we are:

  • Correctly identifying 66 of them as fraudulent
  • Missing 9 fraudulent transactions
  • At the cost of incorrectly flagging 441 legitimate transactions

In the real world, one would put an even higher weight on class 1, so as to reflect that False Negatives are more costly than False Positives.

Next time your credit card gets declined in an online purchase – this is why.

Example available on HuggingFace.

Imbalanced classification: credit card fraud detection
Introduction
First, vectorize the CSV data
Prepare a validation set
Analyze class imbalance in the targets
Normalize the data using training set statistics
Build a binary classification model
Train the model with class_weight argument
Conclusions