Binary Classification
Binary classification refers to those classification tasks that have two class labels.
Examples include:
- Email spam detection (spam or not).
- Churn prediction (churn or not).
- Conversion prediction (buy or not).
Typically, binary classification tasks involve one class that is the normal state and another class that is the abnormal state.
For example “not spam” is the normal state and “spam” is the abnormal state. Another example is “cancer not detected” is the normal state of a task that involves a medical test and “cancer detected” is the abnormal state.
The class for the normal state is assigned the class label 0 and the class with the abnormal state is assigned the class label 1.
Multi-Class Classification
Multi-Class Classification refers to those classification tasks that have more than two class labels.
Examples include:
- Face classification.
- Plant species classification.
- Optical character recognition.
Unlike binary classification, multi-class classification does not have the notion of normal and abnormal outcomes. Instead, examples are classified as belonging to one among a range of known classes.
The number of class labels may be very large on some problems. For example, a model may predict a photo as belonging to one among thousands or tens of thousands of faces in a face recognition system.
Problems that involve predicting a sequence of words, such as text translation models, may also be considered a special type of multi-class classification. Each word in the sequence of words to be predicted involves a multi-class classification where the size of the vocabulary defines the number of possible classes that may be predicted and could be tens or hundreds of thousands of words in size.