Binary classification

See also: Machine learning terms

Binary classification is a type of machine learning problem in which the goal is to classify input data into two classes or categories. Often, these classes are labeled as positive (1) and negative(0), or true(1) and false(0) respectively.

Binary classification problems require input data in various forms, such as text, images, audio or numerical. The classification model learns from labeled training data where each data point is associated with a label indicating which class it belongs to.

Constructing a binary classification model typically involves selecting an appropriate algorithm, preprocessing input data, specifying features to be included in the model, training and assessing its performance. Popular algorithms used for binary classification include logistic regression, decision trees, random forests and support vector machines (SVM).

The performance of a binary classification model is typically assessed using metrics such as accuracy, precision, recall and F1 score. The appropriate evaluation metric depends on the specific problem at hand and how important correctly identifying each class is for success.

Examples of binary classification problems include spam email detection, fraud detection, disease diagnosis and sentiment analysis.