![]() ![]() The document or video may be about one of ‘religion’, ‘politics’, ‘finance’ or ‘education’, several of the topic classes, or all of the topic classes. For example, prediction of the topics relevant to a text document or video. ![]() This approach treats each label independently whereas multilabel classifiers may treat the multiple classes simultaneously, accounting for correlated behavior among them. It is thus comparable to running n_classes binary classification tasks, for example with sklearn. Positive classes are indicated with 1 and negative classes with 0 or -1. Formally, binary output is assigned to each class, for every sample. This can be thought of as predicting properties of a sample that are not mutually exclusive.
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January 2023
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