Regression versus classification

Amazon ML does two types of predictive analytics: classification and regression.

As discussed in the preceding paragraph, classification is about predicting a finite set of labels or categories for a given set of samples.

• In the case of two classes, the problem is called Binary classification
• When there are more than two classes and the classes are mutually exclusive, the problem is a multiclass classification problem
• If the samples can belong to several classes at once, we talk about a multilabel classification problem

In short, classification is the prediction of a finite set of classes, labels, categories.

• Examples of Binary classification are: buying outcome (yes/no), survival outcome (yes/no), anomaly detection (spam, bots), and so on
• Examples of multiclass classification are: classifying object in images (fruits, cars, and so on), identifying a music genre, or a movement based on smartphone sensors, document classification and so on

In regression problems, the outcome has continuous values. Predicting age, weight, stock prices, salaries, rainfall, temperature, and so forth are all regression problems. We talk about multiple regression when there are several predictors and multivariate regression when the predictions predict several values for each sample. Amazon ML does univariate regression and classification, both binary and multiclass, but not multilabel.