---
title: "Difference between classification and regression in machine learning."  
description: "Difference between classification and regression in machine learning."  
author: "Mukul Goenka"  
published: 2024-04-12  
updated: 2024-04-19  
canonical: https://www.mindstick.com/forum/160616/difference-between-classification-and-regression-in-machine-learning  
category: "artificial intelligence"  
tags: ["artificial intelligence", "ai", "machine learning"]  
reading_time: 2 minutes  

---

# Difference between classification and regression in machine learning.

[Difference](https://www.mindstick.com/articles/157114/good-news-or-bad-news-and-the-difference-is) between [classification](https://www.mindstick.com/blog/11071/clustering-and-classification-with-mahout) and [regression](https://www.mindstick.com/blog/304034/regression-and-classification-in-machine-learning-difference) in [machine learning](https://www.mindstick.com/articles/13070/rising-popularity-of-machine-learning-classes-in-bangalore).

## Replies

### Reply by Bhavesh Badani

Let’s break down the difference between **classification** and **regression** in a way that’s easy to grasp:

**Classification**:

- Imagine you’re sorting fruits into different baskets based on their color. You have red apples, green apples, and yellow bananas.
- **Goal**: To put each fruit into the right basket (class).
- **What it predicts**: Discrete labels like “Apple” or “Banana.”
- **Examples**:

   - **Email Spam Detection**: Deciding whether an email is spam or not.
   - **Gender Prediction**: Determining if a name belongs to a male or female.

- Think of it as sorting things into neat categories, like organizing your closet.

**Regression**:

- Picture yourself predicting the temperature for tomorrow. You look at past weather data and patterns.
- **Goal**: To estimate a continuous value (like temperature).
- **What it predicts**: Real numbers (e.g., 75°F).
- **Examples**:

   - **House Price Prediction**: Guessing the price of a house based on its features.
   - **Stock Market Forecast**: Predicting stock prices.

- It’s like drawing a line through scattered points on a graph to make predictions.

In summary:

- **Classification** deals with categories (yes/no, spam/not spam).
- **Regression** deals with numbers (temperature, price).


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