Многоклассовая классификация — supervised learning task, в которой каждому примеру назначается один класс из трёх или более возможных классов. Она отличается от binary classification, где есть только два класса, и может использовать methods such as softmax outputs или one-vs-rest classifiers.
In supervised learning, a classification problem in which the dataset contains more than two classes of labels. For example, the labels in the Iris dataset must be one of the following three classes: - Iris setosa - Iris virginica - Iris versicolor A model trained on the Iris dataset that predicts Iris type on new examples is performing multi-class classification. In contrast, classification problems that distinguish between exactly two classes are binary classification models. For example, an email model that predicts either spam or not spam is a binary classification model. In clustering problems, multi-class classification refers to more than two clusters. See Neural networks: Multi-class classification in Machine Learning Crash Course for more information.
A classification case where the label is one out of three or more classes. For more information, see the Multiclass classification section of the Machine learning tasks topic.
A machine learning model that predicts values that belong to a limited, pre-defined set of permissible values. For example, "Is this product a book, movie, or clothing?"