# Classification vs Regression: An Ultimate Guide in Machine learning

Safalta Expert Published by: Aryan Rana Updated Sat, 10 Dec 2022 12:03 AM IST

## Highlights

Even the most experienced data scientists might occasionally become perplexed when comparing regression and classification in machine learning. Regression and classification are examples of supervised machine learning methods, in which a model is taught using both correctly labelled data and the pre-existing model for each particular case.

Table of Content
How Machine Learning's Regression and Classification Differ
What is Regression Machine Learning?
What is Classification Machine Learning?
Categorization using a decision tree
Using a random forest, classify
What is Regression Machine Learning?
Regression vs Classification in Machine Learning: Understanding the Difference

Even the most experienced data scientists might occasionally become perplexed when comparing regression and classification in machine learning. They may eventually find it challenging to apply the appropriate approaches to solve prediction difficulties as a result of this. Regression and classification are examples of supervised machine learning methods, in which a model is taught using both correctly labelled data and the pre-existing model. Regression and classification algorithms have many similarities, but there are also numerous differences that you should be aware of in order to execute them appropriately and improve your machine-learning abilities. The distinction between regression and classification algorithms will be clarified in this blog post.

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How Machine Learning's Regression and Classification Differ

In the domains of AI and data science, a thorough understanding of both is essential because some algorithms may require both classification and regression techniques.

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Prior to delving deeply into the distinctions between regression and classification techniques. Let's first examine each algorithm in detail.

What is Regression Machine Learning?

A continuous value is predicted by regression methods using the input variables. Estimating a mapping function based on the input and output variables is the primary objective of regression issues. Use a regression model if your goal variable is a number, such as income, grades, height, or weight, or a probability, such as the likelihood that it will rain in a given area. However, data scientists and ML engineers utilise a variety of regressions depending on the context. Regression algorithms come in various forms, including:
• Simple linear regression
• Given that both variables are quantitative, simple linear regression allows you to estimate the relationship between one independent variable and another dependent variable using a straight line.
• Multiple linear regression
• Multiple regression is an extension of simple linear regression that predicts the values of a dependent variable in light of the values of two or more independent variables.
• Polynomial regression
• Modelling or identifying a nonlinear relationship between dependent and independent variables is the primary goal of polynomial regression.

What is Classification Machine Learning?

A classification model predicts discrete output variables, such as labels or categories, by approximating a mapping function from input variables. In classification algorithms, the mapping function is in charge of foretelling the label or category of the provided input variables. Discrete and real-valued variables can both be used in a classification process, but it still needs the instances to fall into one of at least two classes.

The various kinds of categorization algorithms consist of:

Categorization using a decision tree

A classification model is built using this approach by constructing a decision tree, where each node represents a test case for an attribute and each branch leads to a potential value for that attribute.

Using a random forest, classify

A portion of the main training set's decision trees is randomly chosen for this tree-based algorithm's use. The final output prediction made by the random forest classification algorithm, which is more accurate than any of the individual trees, is made by combining the outputs from all the various decision trees.

A classification model is built using this approach by constructing a decision tree, where each node represents a test case for an attribute and each branch leads to a potential value for that attribute.

Using a random forest, classify

A portion of the main training set's decision trees is randomly chosen for this tree-based algorithm's use. The final output prediction made by the random forest classification algorithm, which is more accurate than any of the individual trees, is made by combining the outputs from all the various decision trees.

What is Regression Machine Learning?

A continuous value is predicted by regression methods using the input variables. Estimating a mapping function based on the input and output variables is the primary objective of regression issues. Use a regression model if your goal variable is a number, such as income, grades, height, or weight, or a probability, such as the likelihood that it will rain in a given area. However, data scientists and ML engineers utilise a variety of regressions depending on the context. Regression algorithms come in various forms, including:

Regression vs Classification in Machine Learning: Understanding the Difference

The most significant difference between regression and classification is that while regression helps predict a continuous quantity, classification predicts discrete class labels. There are also some overlaps between the two types of machine learning algorithms.

• A regression algorithm can predict a discrete value which is in the form of an integer quantity
• A classification algorithm can predict a continuous value if it is in the form of a class label probability

Let’s consider a dataset that contains student information about a particular university. A regression algorithm can be used in this case to predict the height of any student based on their weight, gender, diet, or subject major. We use regression in this case because height is a continuous quantity. There is an infinite number of possible values for a person’s height.