## Abstract

Classification tries to predict a class from a data item. **Regression** tries to predict a value. For example, we know the zip code of a house, the square footage of its lot, the number of rooms, and the square footage of the house, and we wish to predict its likely sale price. As another example, we know the cost and condition of a trading card for sale, and we wish to predict a likely profit in buying it and then reselling it. As yet another example, we have a picture with some missing pixels— perhaps there was text covering them, and we want to replace it—and we want to fill in the missing values. As a final example, you can think of classification as a special case of regression, where we want to predict either + 1 or − 1; this isn’t usually the best way to proceed, however. Predicting values is very useful, and so there are many examples like this.