Classification predictive modeling problems are different from regression predictive modeling problems.
- Classification is the task of predicting a discrete class label.
- Regression is the task of predicting a continuous quantity.
There is some overlap between the algorithms for classification and regression; for example:
- A classification algorithm may predict a continuous value, but the continuous value is in the form of a probability for a class label.
- A regression algorithm may predict a discrete value, but the discrete value in the form of an integer quantity.
Some algorithms can be used for both classification and regression with small modifications, such as decision trees and artificial neural networks. Some algorithms cannot, or cannot easily be used for both problem types, such as linear regression for regression predictive modeling and logistic regression for classification predictive modeling.
Importantly, the way that we evaluate classification and regression predictions varies and does not overlap, for example:
- Classification predictions can be evaluated using accuracy, whereas regression predictions cannot.
- Regression predictions can be evaluated using root mean squared error, whereas classification predictions cannot.