What is the difference between regression and classification?

Regression and classification are both types of supervised learning tasks in machine learning, where a model learns from labelled training data to make predictions on unseen data.

The main difference between them lies in the type of output or prediction they make.

Regression: In a regression task, the model is trained to predict a continuous or quantitative output.

For example, predicting the price of a house based on its features (like size, location, number of rooms, etc.) is a regression task because the price is a continuous quantity that can range from any value to any value.

Classification: In a classification task, the model is trained to predict a discrete or categorical output. For example, predicting whether an email is spam or not spam is a classification task because the output (spam or not spam) is a category.

Use regression when you want to predict a quantity (like house prices, temperatures, sales amounts, etc.) and use classification when you want to predict a category (like spam or not spam, disease or no disease, pass or fail, etc.).