Based on the possibility of class output, machine learning classification can be categorized into binary classification, multiclass classification, and multilabel classification, as follows:
- Binary classification: This classifies observations into one of two possible classes. The example of spam email filtering we mentioned earlier is a typical use case of binary classification, which identifies email messages (input observations) as spam or not spam (output classes). Customer churn prediction is another frequently mentioned example, where the prediction system takes in customer segment data and activity data from CRM systems and identifies which customers are likely to churn. Another application in the marketing ...
