R Programming

Classification vs. Regression Models

While building any predictive model, it is important to first understand whether it is a classification or a regression problem. Let’s understand the difference between the two:

1. Classification

In a classification problem, we are trying to predict the class of a data point (discreet number of values). The Y variable that we are trying to predict generally comes in categorical form and has a finite number of classes. For example, we can classify a loan as Default or No Default. Or we can classify an image as a cat or a dog. The credit risk problem that we are trying to solve is a classification problem. We call it a binary classification when there are only one of the two classes to predict (Default or No Default - 0 or 1). If we have more than 2 classes, we call it a multi-classification problem. Such models are commonly referred to as "classifiers".

2. Regression

The problem we are solving is considered a regression problem if we are predicting a continuous valued output, for example, predicting the price of a house, or stock prices.

When we are solving a data science problem, we will first define our problem as a classification or a regression problem, depending on the output that we are trying to predict.

In our case, we can conclude that predicting default is a classification problem. Let’s now start with our first case study and understand the steps involved in model building.

Finance Train Subscription

Unlock full access to Finance Train and see the entire library of member-only content and resources.