What is classification vs regression?

Classification predicts a discrete category or class label — the output is one of a finite set of values. Examples: email is spam or not spam, image is a cat or dog. Common algorithms: logistic regression, decision trees, random forests, SVMs. Regression predicts a continuous numerical value. Examples: house price, stock value, temperature tomorrow. Common algorithms: linear regression, polynomial regression, gradient boosting. The choice of loss function and evaluation metric differs between the two tasks.