Binary Classification
A machine learning task where a model predicts one of two possible classes, such as spam or not spam.
What is Binary Classification?
Binary classification models learn from labeled training data containing two distinct outcomes, such as positive or negative, fraud or legitimate, or spam or not spam. The model identifies patterns in the input features and typically predicts either a class label or the probability that an input belongs to a particular class.
Why is Binary Classification Important?
Many real-world decisions involve choosing between two possible outcomes. Binary classification enables organizations to automate these decisions at scale and consistently evaluate new data. Model performance can be measured using metrics such as accuracy, precision, recall, F1 score, and ROC curves.
Common use cases
Binary classification is commonly used in fraud detection, spam filtering, medical diagnosis, credit risk assessment, customer churn prediction, and cybersecurity threat detection.