Multi-class Classification
Multi-class classification is a supervised learning task in which an algorithm is trained to classify instances into one of three or more classes. The goal is to predict the correct class label for new, unseen instances. In multi-class classification, each instance is assigned to one and only one class, unlike multi-label classification where an instance can belong to multiple classes. There are several algorithms that can be used for multi-class classification, including decision trees, random forests, support vector machines, and neural networks. Evaluation metrics for multi-class classification include accuracy, precision, recall, and F1-score.
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