Binary Classification is a machine learning approach used to assign data to one of two possible categories. The model learns from labeled examples and predicts outcomes such as spam or legitimate email, fraudulent or valid transactions, or defective and non-defective products. Unlike multiclass classification, which distinguishes among several categories, binary classification focuses on a two-way decision.
The technique is commonly implemented with algorithms such as logistic regression, decision trees, support vector machines, and neural networks. In practical systems, the model often produces a probability score that is converted into a class using a chosen threshold. This threshold can be adjusted when false positives and false negatives have different consequences.
