What is Supervised Learning?

By NIIT Editorial

Published on 02/03/2021

5 minutes

Machine learning, as a subset of Artificial Intelligence, has multiple segments one of which happens to be Supervised Learning. Yet, the concept seems alien to a good amount of non-technical readership. In this article, we’ll offer a simple explanation of what supervised learning is and how it works. 


What is Supervised Learning?  


It is a technique of training a machine learning algorithm in a supervised procedure. The code does not understand the requirements, so it has to be first trained in the skill of classifying information with the help of labeled information. Once enough training has been delivered, the algorithm is capable of performing a certain level of identification on its own. Let us understand this with a simple example. 


You want the algorithm to be able to tell the fruits by their name. Therefore, it will be taught first how to mark the specifications of each fruit using labelled data. You can define an apple as: 


Being round, or oval 

Red, or pink in color


Similarly, you can define a banana as: 


Being oblong in shape 

Being yellow in color


After training the data, if you feed it a clear image of apple and or banana, it will be able to tell the answer with accuracy. This happened because it had ample training data to work on, that can be applied to the test data. 


Supervised learning can be further divided into two categories which essentially refer to the type of problems the algorithm is supposed to solve: 


Classification - This is the type of problem wherein the algorithm is supposed to classify categories such as red, yellow, blue etc. 


Regression - In this type, the algorithm is supposed to classify, if the output variable is a real-world problem. 


The main advantages of supervised learning is that it makes algorithms self-sufficient to a certain degree. Thanks to regression learning, machine learning algorithms can be taught to solve real world problems as well.




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