Which algorithm is commonly used to address multi-class classification problems?

Multinomial Logistic Regression is specifically designed for multi-class classification problems, where the goal is to categorize instances into one of three or more classes. Unlike binary logistic regression, which only predicts two classes, multinomial logistic regression can handle multiple categorical outcomes.

The model operates by extending logistic regression to multiple classes, allowing it to provide probabilities for each class, based on the input features. This is done using a softmax function, which normalizes the scores across all classes to ensure that they sum to one. As a result, the prediction can be interpreted in terms of probabilities for each of the possible classes.

In contrast, while Logistic Regression can solve binary classification tasks effectively, it is not suitable for problems involving more than two classes unless modified (e.g., through one-vs-rest strategies). Support Vector Machines can be adapted for multi-class classification, but the basic formulation is primarily for binary classification, requiring additional approaches to handle multiple classes. K-Nearest Neighbors can also be used for multi-class classification, but it does not optimize the class prediction through a probabilistic framework like multinomial logistic regression does.