The difference between regression and classification in Machine Learning

In Machine Learning (ML), supervised learning tasks are divided into regression and classification problems. Both of the learning methods invlove learning from the labeled data, they serve dffernet purposesand are applied in distinct scenarios. Undestanding the difference between regression and classification is crucial for selecting the right approach for your ML project. In this post we will explore what regression and classification mean, how they are different, and provide examples and algorithms for each.

What is Regression?

Regression is a type of supervised learning where the goal is to predict a continuous numerical value. The model learns to establish a relationship between input features (input variables) which are independent variables and continuous otuput label (the dependent variable).
The key features of regression are:

• Output - a continuos number
• Goal - Minimize the error between predicted values and true values (e.g. Mean Squared Error or Mean Absolute Error)
• Application - Regression is used in scenarios where the otucome is a number that can take any value within a range.

The examples of regression are the prediction the house pricing based on features like size, location, number of bedrooms. The second example is the estimation of the temperature for a given day based on weather condition. Forecasting sales revenue of the next quarter. The common regression algorithms are

• Linear regression,
• Polynomial regression,
• Support Vector Machines (SVM),
• Decision Tree Regressor (DTR),
• Random Forest Regressor (RFR),
• Neural network (e.g. Multi Layer Perceptron Regressor)

SVM , DTR, RFR (for regression), MLP regressor

What is Classification?

The classification is another type of supervised learning where the goal is to predict hte categorical otuput. The model learns to classify input data into one of several predefined lasses.

The key features of classification

The output of hte unsupervised learning algoiithm is the a discrete label or a class. The goal is to maximize the accuracy of class predictions. The classification is used when the output is a category or a label.
The examples of the classificaiton problems are indetifying whether an email is spam or not spam. The Classifying handwritten digtis into numbers (0-9). Predicting wheter a patienthas a disease.
Common classification algorithsm are:

Logistic regression

k-Nearest Neighbors (kNN),

Support Vector Machines (SVM),

Decision Tree Classifier,

Random Forest Classifier, and

Neural Networks (e.g. Multi-Layer Perceptron Classifier)

Comparison of Regression and Classification

Aspect	Rregression	Classification
Output Type	Continuous (real numbers)	Discrete (categories or labels)
Goal	Predict numerical values	Predict class labels
Evaluation Metrics	Mean Squared Error, Mean Absolute Error, Mean Absolute Percentage Error, \(R^2\)	Accuracy, Area uder ROC, Precision, Recall, F1-Score
Examples	Predicting house prices, stock trends	Spam Detection, image recognition
Common Algorithms	Linear Regression SVM Neural Networks	Logistic Regression, k-NN, Random Forest Classifier

A practical example

To better understand the difference between regression and classification let's consider a dataset containing the information about cars. The features (input variables) are Engine Size, Fuel Efficiency, Age. In case of the regression the output (target variable) would be car car prices and the goal is to predict the car price for given features (input variables).
In case of the classification the goal could be to predict the car type based on the features (input variables): engine size, fuel efficiency, and age.

Hybrid Scenarios

In some cases, regression and classification can work together: A multi-stage model might first use regression to predict a numerical score and then classify the result into categories based on thresholds. For example, predicting customer lifetime value (regression) and classifying them into "high-value" or "low-value" customers.

Conclusion

Regression and classification are the cornerstones of supervised learning, each designed to solve different types of problems. While regression predicts continuous outcomes, classification assigns data to discrete categories. Choosing the right approach depends on the nature of your problem and the type of output you need. Understanding the difference will help you design better models and apply the right algorithms for your machine learning projects. Got questions about regression or classification? Drop them in the comments!