As with regressors, classifiers can also have multiple targets. Additionally, due to their discrete targets, a single target can have two or more values. To be able to differentiate between the different cases, machine learning practitioners came up with the following terminologies:
- Multi-class
- Multi-label (and multi-output)
The following matrix summarizes the aforementioned terminologies. I will follow up with an example to clarify more, and will also shed some light on the subtle difference between the multi-label and multi-output terms later in this chapter:
Imagine a scenario where you are given a picture and you need to classify it based on whether it contains a cat or not. In this case, a binary classifier is needed, that is, where the targets are either zeroes or ones. When the problem involves figuring out whether the picture contains a cat, a...
