Classification vs Regression

Classification and regression are two core types of tasks in supervised learning, which is a branch of machine learning where the model learns from labeled data. The main difference between them lies in the output type they are predicting.

Classification

Classification tasks involve categorizing input data into predefined classes or labels. The output variable in classification is discrete, meaning it takes on a limited number of categorical values. Classification can be binary (e.g., spam or not spam) or multiclass (e.g., predicting the type of fruit based on its characteristics).

Examples:

Email spam detection (spam or not spam).
Image recognition (categorizing images into predefined categories).
Disease diagnosis (determining the type of disease from symptoms).

Regression

Regression tasks predict a continuous quantity. The output variable in regression is a real value, which means it can take on any number within a range. Regression is used when the goal is to predict a quantity rather than a category.

Examples

Predicting house prices based on features like size, location, and number of bedrooms.
Estimating a person's age based on their physical characteristics.
Forecasting stock prices.