Only a few classification models aid multi-class classification. Specific algorithms, including logistic regression and perceptron, work best with binary classification and do not support more than two classes of classification tasks. The best alternative for solving multi-class classification problems is splitting the multi-class datasets into multiple binary assemblies of data that can fit the binary classification model.

Algorithms used in binary classification problems cannot work with multi-class tasks. Therefore, heuristic methods, such as one-vs-one and one-vs-rest, are used to split multi-class problems into multiple binary datasets and train the binary classification model.

Binary vs. Multi-Class Classification

Classification problems are common in machine learning. In most cases, developers prefer using a supervised machine-learning approach to predict class tables for a given dataset. Unlike regression, classification involves designing the classifier model and training it to input and categorize the test dataset. For that, you can divide the dataset into either binary or multi-class modules.

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