Description: Multinomial logistic regression is an extension of logistic regression used to address multiclass classification problems. Unlike binary logistic regression, which is limited to two categories, multinomial logistic regression allows for classifying observations into three or more groups. This model is based on the relationship between a categorical dependent variable and one or more independent variables, which can be continuous or categorical. The technique uses the logistic function to model the probability that an observation belongs to a specific category, considering the relative probabilities of each class. One of the key features of multinomial logistic regression is that it is based on the principle of maximum likelihood, meaning it seeks to estimate the model parameters that maximize the probability of observing the data given the parameters. This approach is particularly useful in situations where classes are not mutually exclusive and allows for a richer interpretation of the data. Additionally, multinomial logistic regression is widely used in data analysis across various disciplines, including business, healthcare, and social science, due to its ability to effectively handle multiple categories.
