The Fundamentals of Classification

As stated earlier, the goal of any classification problem is to separate the data into relevant groups accurately using a training set. There are a lot of applications of such projects in different industries, such as education, where a model can predict whether a student will pass or fail an exam, or healthcare, where a model can assess the level of severity of a given disease for each patient.

A classifier is a model that determines the label (output) or value (class) of any data point that it belongs to. For instance, suppose you have a set of observations that contains credit-worthy individuals, and another one that contains individuals that are risky in terms of their credit repayment tendencies.

Let's call the first group P and the second one Q. Here is an example of such data:

Figure 3.1: Sample dataset

With this data, you will train a classification model that will be able to correctly classify a new observation...