What is a multi-class classification?
In supervised learning, a classification problem in which the dataset contains more than two classes of labels.
multi-class classification explained in plain English
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.
Example
Practitioners refer to multi-class classification when building, training, or evaluating machine learning systems. It appears in research papers, product documentation, and technical discussions about AI capabilities and limitations.
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