Бинарная классификация — задача машинного обучения, в которой каждый пример относится к одному из двух возможных классов. Типичные примеры включают спам и не спам, мошенничество и легитимную операцию либо положительный и отрицательный исход. Производительность модели часто оценивается с помощью метрик вроде accuracy, precision, recall, F1 score или 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).