Simply Curious

Learning and applying machine learning can be completely different things. When we learn machine learning, we get to know about the different algorithms, their hyperparameters, the type of problems they are most suited for, the mathematics behind them and so on. Applying machine learning to a business problem though, is hardly about the algorithm. Rather, it involves solving harder problems that one rarely gets to learn in academia. Let’s look at an example using a problem where machine learning is frequently applied: credit card fraud detection. The credit card fraud detection problem has a very simple premise: given a host of attributes about a credit card transaction, predict whether it is a fraudulent one. This could be a binary classifier that takes in a transaction and gives the probability of it being fraudulent. It’s important to achieve both a reasonably good precision and recall for such a use case. Why? Low precision here would mean that we block too many non-fraudulent tran