This article explains the Machine Learning term Binary Classification and Binary Classification as a concept within the AI & Analytics Engine
Binary Classification is a type of Classification task, where there are only two possible classes or outcomes to predict. Additionally, between the two possible classes, one class is the outcome of interest. The outcome of interest is known as a positive class label. In simpler terms, it is a kind of problem where one needs to accurately predict the answer to a yes or no question about entities.
Some examples of Binary Classification are:
Predicting whether a customer will default his mortgage repayments,
Predicting whether a person will exit his internet plan upon expiry of his contract, and
Predicting whether a credit-card transaction is normal or the result of card theft
Binary Classification within the AI & Analytic Engine
Within the Engine, while creating an app, if the user chooses the “Predict a variable/column” problem type, Binary Classification is available as the “Answer a yes/no question” problem sub-type.
This option is automatically recommended by the Engine when there are only two possible outcomes. The positive class label is automatically chosen to be the rarer of the two class labels, based on their frequencies of occurrence in the dataset.
💡For further information about Binary Classification models, check out: