As with regression, classification problems come with their own set of jargon. There is some overlap with terms used in regression, but there are also some new terms specific to classification:
- Categories, labels, or classes: These terms are used interchangeably to represent the various distinct choices for our prediction. For example, we could have a fraud class and a not fraud class, or we could have sitting, standing, running, and walking categories.
- Binary classification: This type of classification is one with only two categories or classes, such as yes/no or fraud/not fraud.
- Multi-class classification: This type of classification is one with more than two classes, such as a classification trying to assign one of hot dog, airplane, cat, and so on, to an image.
- Labeled data or annotated data: Real-world observations or records that...
