Classification vs Regression: Understanding the Distinction | Let’s Decode | Sep 2024

In machine learning, both classification and regression are crucial predictive modeling techniques. Imagine being a fortune teller, only instead of using crystal balls, you use statistics to predict the future. Sounds intriguing, doesn’t it?

Now, let’s say you’re predicting whether India will win the upcoming World Cup or Not(Yes or No). That’s a classification problem. If you’re predicting how many matches they’ll win consecutively, now we’re talking about regression. They may sound similar, but there’s a huge difference between them.

Let’s dive into each type, break down the differences, and understand when and why you’d use one over the other.

In simple words, Classification is like putting things into boxes. You have a bunch of data points, and your job is to put each one into a specific category or class. It’s about making choices: Is it A or B? Yes or No? Cat or Dog? I think you get the idea!

• Email Filtering: Is an email spam or not spam?
• E-commerce: Should I buy this product or not?

A machine learning model learns from labeled data(We will discuss what labeled data is in future articles). For example, if we show the model 1000 pictures of cats and dogs (with labels), it will learn what makes a cat a cat and a dog a dog. Once trained, the model can then predict whether a new image is a cat or a dog.

In technical terms, Classification algorithms predict a discrete output (like a class or category). These classes are predefined, and the model’s goal is to correctly assign new data points to one of these classes. For example:- For the email spam model, the model was trained on a huge labeled data by which whenever a new email comes it predicts whether the email is spam or not.

In simple words, Regression is all about predicting numbers. Instead of saying “Yes” or “No,” you’re predicting how much, how far, or how fast. It’s the part of machine learning where the output isn’t a class but a continuous number.

• House Price Prediction: Based on features like the size, location, and number of rooms, how much will this house sell for?
• Stock Market Prediction: What will the stock price be in the next hour, day, or week?

Similar to classification, regression models learn from labeled data, but here, the data isn’t about categories — it’s about numerical values. For example, if you feed the model data on house prices based on various features(size, location, no. of bedroom etc.), the model will learn to predict prices for new houses.

In technical terms, Regression algorithms predict a continuous output — a number with a decimal or range, like predicting temperature, sales, or time. For example:- For a temperature prediction model, the model was trained on a huge labeled dataset by which it can predict tomorrow’s temperature based on different features like country, state, city, weather condition etc.

Let’s break down the main differences between these two approaches to make things crystal clear:

Now you understand what regression and classification are, and their differences. But you are confusing when to use classification and when to use regression. Let’s clear up your confusion:-

Use Classification When:

• You want to predict categories or labels.
• The output is discrete, meaning it falls into a limited number of classes.
• Examples: Will it rain tomorrow? (Yes/No), Is this a picture of a cat or dog?

Use Regression When:

• You want to predict a number or continuous value.
• The output is numeric, and the result can be anywhere along a continuous range(any real number).
• Examples: How much will this house sell for? What will the temperature be tomorrow?

Finally, classification and regression are two sides of the same coin in machine learning. Classification helps you predict categories (e.g., dog or cat), while regression helps you predict numbers (e.g., house prices). They may seem similar, but each technique is used for solving different problems.

If you’re looking to categorize data, go for classification. If you want to predict a numerical value, go for regression. And just like that, you’ll be using machine learning like a pro!

P.S. If you learn something new from this article, please show your support by giving it a clap 👏. Your appreciation motivates me to create more articles for you.

“Which technique you will learn first 🤷‍♂️? Share your thoughts 💭”