Multinomial Logistic Regression is used to classify linearly separable classes. It predicts the probability of the class of a categorical variable given a set of variables (features). Multinomial Logistic Regression extends the application of Logistic Regression to more than two classes.

Logistic Regression is a binary classifier so it classifies data into two classes. The classifier outputs the probability mass function of a datapoint over the classes, namely, a vector of values stating the probability of the datapoint belonging to each class. If the probability is above a specific threshold, it will assign the datapoint to that class. Multinomial Logistic Regression uses the same process but for more than two classes. In this notebook, we see how to implement multinomial logistic regression using the wine dataset from SciKit Learn.

Image From: Murphy, K. P. (2021). Figure 1.19. *In Machine Learning: A Probabilistic Perspective*. textbook, MIT Press.