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What is Supervised Learning?

by Bhumika

As the name implies, supervised learning involves the presence of a supervisor who also serves as an instructor. In a nutshell, supervised learning (SL)is when we instruct or train a machine using well-labeled data. This signifies that some information has already been label with the correct answer. The machine is then gave a new collection of examples (data) to analyze the training data (set of training examples) and produce a proper result from label data using these method. 

It is a sort of machine learning in which machines are train using well-labeled training data and then predict the output based on that data. The label data indicates that some of the input data has already tagged with the appropriate output.

In these, the training data presented to the machines acts as a supervisor, instructing the machines on how to correctly predict the output. It uses the same notion as when a student learns under the guidance of a teacher.

Types of supervised learning

There are two types of algorithms for this learning:

Regression  – When the output variable is a real value, such as “dollars” or “weight,” a regression problem exists. If there is a relationship between the input and output variables, regression procedures are apply. It’s uses to predict continuous variables like weather forecasting, market trends, and so on. 

Classification  – When the output variable is a category, such as “Red” or “Blue,” or “illness” and “no disease,” the problem is called a classification problem.

When the output variable is categorical, meaning there are two classes, such as Yes-No, Male-Female, True-False, and so on, classification methods are utilize.


  • The model can predict the result based on prior experiences thanks to SL.
  • We can have a precise concept about the classes of things via SL.
  • We may use the SL model to handle a variety of real-world problems, such as fraud detection and spam filtering.


  • Models of SL are ineffective for dealing very complicated tasks.
  • If the test data differs from the training dataset, these learning will not be able to predict the proper output.
  • Training necessitated a significant amount of computation time.
  • In these , we must have a sufficient understanding of the object classes.

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