Binary Classification
Binary classification is a supervised learning technique in which the goal is to predict a binary outcome (1 or 0, true or false, positive or negative) based on a set of input features. The algorithm learns from a labeled dataset, where each observation is labeled as either positive or negative. The model then uses this learning to predict the outcome of new, unseen data. Binary classification is widely used in various fields, such as finance, healthcare, and marketing, to predict outcomes such as fraud detection, disease diagnosis, and customer churn. Common algorithms used for binary classification include logistic regression, decision trees, and support vector machines.
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