Binary classification is a machine learning task in which each example is assigned to one of two possible classes. Common examples include spam versus not spam, fraud versus legitimate, or positive versus negative outcomes. Model performance is often evaluated with metrics such as accuracy, precision, recall, F1 score, or AUC.
A classification case where the label is only one out of two classes. For more information, see the Binary classification section of the Machine learning tasks topic.
A type of classification task that predicts one of two mutually exclusive classes: - the positive class - the negative class For example, the following two machine learning models each perform binary classification: - A model that determines whether email messages are spam (the positive class) or not spam (the negative class). - A model that evaluates medical symptoms to determine whether a person has a particular disease (the positive class) or doesn't have that disease (the negative class). Contrast with multi-class classification. See also logistic regression and classification threshold. See Classification in Machine Learning Crash Course for more information.
Amazon Machine Learning: A machine learning model that predicts the answer to questions where the answer can be expressed as a binary variable. For example, questions with answers of "1" or "0", "yes" or "no", "will click" or "will not click". The result is always either a "1" (affirmative) or a "0" (negative).