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:
- Predicting the species of a flower based on its features, where the species could be one of several different types.
- Classifying emails into different categories such as “spam,” “promotions,” or “personal.”
- 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).