What is Binary Classification?
Binary Classification refers to those classification tasks that have two class labels.
- 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.
It is common to model a binary classification task with a model that predicts a Bernoulli probability distribution for each example.
The Bernoulli distribution is a discrete probability distribution that covers a case where an event will have a binary outcome as either a 0 or 1. For classification, this means that the model predicts a probability of an example belonging to class 1, or the abnormal state.