Multinomial classification is a type of supervised learning task in machine learning where the goal is to classify input instances into three or more possible categories.

Some common use cases of multinomial classification include:

  1. Predicting the species of a flower based on its features, where the species could be one of several different types.
  2. Classifying emails into different categories such as “spam,” “promotions,” or “personal.”
  3. Recognizing handwritten digits from 0 to 9 in optical character recognition (OCR) tasks.

Common algorithms used for multinomial classification include logistic regression (with extensions such as softmax regression), decision trees, random forests, k-nearest neighbors (KNN), support vector machines (SVM), and neural networks (particularly with softmax activation function in the output layer).