Formulating the Problem

The first step in machine learning is to decide what you want to predict, which is known as the label or target answer. Imagine a scenario in which you want to manufacture products, but your decision to manufacture each product depends on its number of potential sales. In this scenario, you want to predict how many times each product will be purchased (predict number of sales). There are multiple ways to define this problem by using machine learning. Choosing how to define the problem depends on your use case or business need.

Do you want to predict the number of purchases your customers will make for each product (in which case the target is numeric and you’re solving a regression problem)? Or do you want to predict which products will get more than 10 purchases (in which case the target is binary and you’re solving a binary classification problem)?

It is important to avoid over-complicating the problem and to frame the simplest solution that meets your needs. However, it is also important to avoid losing information, especially information in the historical answers. Here, converting an actual past sales number into a binary variable “over 10” versus “fewer” would lose valuable information. Investing time in deciding which target makes most sense for you to predict will save you from building models that don’t answer your question.