Classifiers map inputs to fixed labels via encoders and softmax or sigmoid

A classifier assigns an input to a fixed label set, covering binary, multiclass, and multilabel variants, such as spam versus not spam or movie genres.

It encodes the input into a vector using hand-built features like logistic regression or a learned encoder such as a CNN or BERT. A linear layer then projects that vector into K logit scores, which softmax or sigmoid turns into probabilities.