What is the difference between classification and regression?

Regression is the process of estimating the mapping function (f) given the input value (x) to the continuous output value (y). It is used to predict a value given the data. Here, labeled data is used to create a model or function and this function is used to predict the value of unlabeled data. Linear or Logistic regression is a good example of this type.

Classification is the process of categorizing the data. The classification model is created by using the algorithm on the data so it is categorized mainly based on the similarity. The naive Bayes classifier is a good example of this type.