Regression vs. Classification in Machine Learning


The rapidly expanding fields of artificial intelligence and machine learning are to thank for our machines' increasing intelligence and independence. But both fields are extremely complicated, and getting a greater understanding of them requires time and effort.

The methods for regression and classification, which both predict in machine learning and employ labeled datasets, are called supervised learning algorithms. However, their point of departure is how they approach Machine Learning problems differently.

Let's now examine Regression vs. Classification in greater detail. This article examines the definitions, kinds, distinctions, and application cases of regression vs. classification in machine learning.

Regression vs. Classification in Machine Learning


Regression determines whether dependent and independent variables are correlated. Regression algorithms, therefore, aid in predicting continuous variables such as real estate values, economic trends, climatic patterns, oil and gas prices (a crucial task in today's world! ), etc.

The regression procedure aims to identify the mapping function that will allow us to translate the continuous output variable "y" into the input variable "x."


On the other hand, classification is an algorithm that identifies functions that support categorizing the dataset based on different factors. Computer software learns from the training dataset when employing a classification algorithm, then divides the data into several groups based on what it has discovered.

The mapping function that converts the discrete "y" output from the "x" input is found by classification algorithms. Based on a certain set of independent variables, the algorithms estimate discrete values (sometimes known as binary values such as 0 and 1, yes and no, true, or false). To put it another simpler way, classification algorithms determine the likelihood that an event will occur by fitting data to a logit function.


Regression and classification can be performed using a variety of algorithms, each of which has advantages and disadvantages. The most popular algorithms include support vector machines, decision trees, random forests, logistic regression, and linear regression.

The type of data you have is crucial when deciding between regression and classification. Regression is the superior option if your data consists of continuous values. Classification is better if your data is made up of discrete numbers.

Here is the table for the differences −



Predicts continuous values, such as prices or weights.

Predicts discrete values, such as labels or categories.

It uses squared error loss or means absolute error loss.

Uses cross-entropy loss or multiclass log loss.

The goal is to minimize the difference between the predicted and actual values.

The goal is to classify each data point into its respective class accurately.

The model output is a continuous function.

The model output is a probability distribution over classes.

Examples include predicting housing prices, stock prices, etc.

Examples include image classification, spam detection, etc.

Regression algorithms include linear regression, polynomial regression, etc.

Classification algorithms include logistic regression, decision trees, random forests, etc.

Evaluation metrics include R-squared, mean squared error, mean absolute error, etc.

Evaluation metrics include accuracy, precision, recall, F1- score, etc.

A line or curve represents the relationship between the independent and dependent variables.

s the relationship between the independent and dependent variables.

A decision boundary represent Input variables can be either continuous or discrete.

Input variables can be either continuous or discrete.

Multiple input variables can be used to predict a single output.

Multiple input variables can be used to predict a single class label.

The number of classes you are attempting to predict is a crucial factor. If you have a lot of classes, classification could be more challenging and demand more information. On the other hand, the classification might be simpler and quicker if there are fewer classes.

Usage of Regression vs. Classification

When the dataset that corresponds to the response variable needs to be classified, classification trees are used. These classes typically have answers of "Yes" or "No." There are only two classes as a result, and they are incompatible. Of course, there may occasionally be more than two classes, but we apply a different version of the classification tree technique in those situations.

Regression trees are used, though, when the response variables are continuous. We utilize a Regression tree, for instance, if the response variable is the price of an item or the current temperature.


In conclusion, regression and classification are two important tasks in machine learning for different purposes. Regression is used for predicting continuous values, while classification is used for predicting discrete values or class labels. Both tasks require different types of algorithms, loss functions, evaluation metrics, and models to achieve their respective goals. Understanding the difference between regression and classification is crucial in choosing the right algorithm and approach for a specific problem and in interpreting the results obtained from the model.

Updated on: 28-Mar-2023


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