Optimising a Machine Learning Model with the Confusion Matrix

For this explanation let's suppose we were working on a binary classification problem to detect whether or not a transaction is fraudulent. Our model uses characteristics of the user and transaction and returns 1 if the transaction is predicted to be fraudulent and 0 if not. Given that machine learning models are rarely 100% accurate there is going to be a level of risk in deploying this model. If we incorrectly classify a non-fraudulent transaction as fraud then we may well lose that transaction, and possibly even the future customers business. On the other hand, if we incorrectly detect a fraudulent transaction as non-fraudulent then we might stand to lose the value of that transaction. The confusion matrix essentially places the resulting predictions into four groups.