Supervised Learning

This article is based on “ “Introduction to Machine Learning with Python A Guide for Data Scientists by Andreas C. Müller, Sarah Guido” book (chapter 2)

What is Supervised Learning?

Basically, Supervised Learning consists of finding a function that can predict the input values, of course after of training data. There are two type of Supervised Learning: Classification and Regression.

Classification and Regression

Classification

The goal is to predict what is known as a “Class Label”, there are 0/1, yes/no, ham/spam, etc. It could be separated into Binary Classification, that is, two classes (yes or no) or also separated into Multiclass Classification, i.e, more than two classes (eg. large, medium, small) .

Regression

On the other hand, Regression task, unlike the Classification, has the objective of predicting continuous numbers. A clear example could be predicting annual income.

Maybe you are wondering :“When can I use Regression or Classification?” or “Where is the best?” Let me tell you that there is no model better than another, but it will depend on your data

So, an easy way to distinguish between classification and regression tasks is to see if there is any sort of continuity in the output. If there is continuity between the possible outcomes, then it is a regression problem. Think of predicting the annual income…