What is Supervised Machine Learning?

Supervised Machine Learning is where you have input variables (x) and an output variable (Y) and you use an algorithm to learn the mapping function from the input to the output Y = f(X). The goal is to approximate the mapping function so well that when you have new input data (x) you can predict the output variables (Y) for that data.

Supervised learning problems can be further grouped into Regression and Classification problems.

• Regression: Regression algorithms are used to predict a continuous numerical output. For example, a regression algorithm could be used to predict the price of a house based on its size, location, and other features.
• Classification: Classification algorithms are used to predict a categorical output. For example, a classification algorithm could be used to predict whether an email is spam or not.

As the name suggests, Classification is the task of “classifying things” into sub-categories. Classification is part of supervised machine learning in which we put labeled data for training.

The article serves as a comprehensive guide to understanding and applying classification techniques, highlighting their significance and practical implications.