Based on the possibility of class output, machine learning classification can be categorized into binary classification, multiclass classification, and multi-label classification.
Binary classification is the problem of classifying observations into one of the two possible classes. One frequently mentioned example is email spam filtering, which identifies email messages (input or observation) as spam or not spam (output or classes). Customer churn prediction is also a typical use of binary classification, where it takes in customer segment data and activity data from CRM systems and identifies which customers are likely to churn. Another application in the marketing and advertising industry is online ads click-through prediction-whether an ad will be clicked or not, given user's cookie information and browsing history.
Lastly, binary classification has also been employed in the biomedical...