What is Supervised Learning?
Defining Supervised Learning
As the name suggests, the Supervised Learning definition in Machine Learning is like having a supervisor while a machine learns to carry out tasks. In the process, we basically train the machine with some data that is already labelled correctly. Post this, some new sets of data are given to the machine, expecting it to generate the correct outcome based on its previous analysis on the labelled data.
Practice makes one perfect! The same applies to machines as well. As the number of practice samples increases, the outcomes produced by the machine become more accurate.
When do we use Supervised Learning?
Supervised learning develops predictive models to come up with reasonable predictions as a response to newly fed data. Hence, this technique is used if we have enough known data (labeled data) for the outcome we are trying to predict. In supervised learning, an algorithm is designed to map the function from the input to the output.
y = f(x) 
Here, x and y are input and output variables, respectively.
The goal here is to propose a mapping function so precise that it is capable of predicting the output variable accurately when we put in the input variable.
So far in this ‘What is Supervised Learning?’ blog, we learned what supervised learning is. Now, we will go further, exploring its types, advantages and disadvantages, and more. Let’s proceed.
Types of Supervised Learning
There are two types of supervised learning techniques, classification and regression. These are two vastly different methods. But how do we identify which one to use and when? Let’s get into that now.
Supervised Learning classification is used to identify labels or groups. This technique is used when the input data can be segregated into categories or can be tagged. If we have an algorithm that is supposed to label ‘male’ or ‘female,’ ‘cats’ or ‘dogs,’ etc., we can use the classification technique. Here, finite sets are distinguished into discrete labels.
A practical example of the classification technique would be the categorization of a set of financial transactions as fraudulent or non-fraudulent. Some of the common applications built around this technique are recommendations, speech recognition, medical imaging, etc.
Classification is again categorized into three:
Binary classification: The input variables are segregated into two groups.
Multiclass/Multinomial classification: The input variables are classified into three or more groups.
Multilabel classification: Multiclass is generalized as multilabel.
The regression technique predicts continuous or real variables. For instance, here, the categories could be ‘height’ or ‘weight.’ This technique finds its application in algorithmic...