Regression Vs Classification In Machine Learning

Regression and classification are many times confusing to many beginners in the field of Machine learning. Eventually, this will make it impossible for them to adopt the correct methodologies for solving problems with prediction.

Source: Unsplash

Regression and classification are both types of supervised machine learning algorithms, where a model is trained along with correctly labeled data according to the current model.

Before we dig deep into understanding the variations between algorithms for regression and classification. Let’s understand each algorithm first.

Regression Machine Learning Algorithm:

Regression algorithms estimate a continuous value based on the input variables. The primary objective of problems with regression is to approximate a mapping function based on the variables of input and output. If the target variable is a quantity such as income, ratings, height or weight, or a binary category likelihood then the regression model should be used. The 3 types of the regression model are as follows:

1. Simple linear regression:

In this type, you may use a straight line to measure the relationship between one independent variable and another dependent variable using simple linear regression…