This completes our discussion on some representative machine learning algorithms. We will now focus on some extremely crucial issues that we need to keep in mind while we apply these ML algorithms in any application domain. First, we will discuss the concept of overfitting to our training data.
The whole point of presenting our learning algorithm with training data is that it can, in the future, predict labels for data points that it has never seen. The ability of any learning algorithm to apply its learnt set of rules to completely new and unseen data is known as the generalization ability of the algorithm. The aim of training any ML classifier is that it should generalize unseen data well.
Let's briefly go back to an example that we introduced early on in this chapter. When students attend classes, a professor teaches them a concept using some illustrative examples (training data). The students (ML algorithms) are expected to build a mental model out of the information they are...
