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 predicted as False Negatives. However, in our case, that is not so; we do not predict a normal transaction. The transaction is in fact...
