Hands-On Machine Learning for Cybersecurity

Logistic regression classifier – under-sampled data

We are interested in the recall score, because that is the metric that will help us try to capture the most fraudulent transactions. If you think how accuracy, precision, and recall work for a confusion matrix, recall would be the most interesting because we comprehend a lot more.

• Accuracy = (TP+TN)/total, where TP depicts true positive, TN depicts true negative
• Precision = TP/(TP+FP), where TP depicts true positive, FP depicts false positive
• Recall = TP/(TP+FN), where TP depicts true positive, TP depicts true positive, FN depicts false negative

The following diagram will help you understand the preceding definitions:

As we know, due to the imbalance of data, many observations could be ...