What is the difference between classification and prediction?

Classification is the prediction of a categorial variable within a predefined vocabulary based on training examples.

The prediction of numerical (continuous) variables is called regression.

In summary, classification is one kind of prediction, but there are others. Hence, prediction is a more general problem.

Functionality

• Classification is about determining a (categorial) class (or label) for an element in a dataset

• Prediction is about predicting a missing/unknown element(continuous value) of a dataset

Working Strategy

• In classification, data is grouped into categories based on a training dataset.

• In prediction, a classification/regression model is built to predict the outcome(continuous value)

Example

In a hospital, the grouping of patients based on their medical record or treatment outcome is considered classification, whereas, if you use a classification model to predict the treatment outcome for a new patient, it is considered a prediction.

Classification is the process of identifying the category or class label of the new observation to which it belongs. Predication is the process of identifying the missing or unavailable numerical data for a new observation. That is the key difference between classification and prediction. The predication does not concern about the class label like in classification.