Understanding Multiclass Classification
The classification algorithms that we discussed earlier were mostly binary classifiers, where the target variable can have only two categorical values or classes. However, there can be scenarios where we have more than two classes to classify samples into. For instance, given data on customer transactions, the marketing team may be tasked with identifying the credit card product most suitable for a customer, such as cashback, air miles, gas station, or shopping.
Multiclass classification can be broadly divided into the following three categories:
Multiclass classification: Multiclass classification problems involve classifying instances or samples into one class out of multiple classes (more than two). Each sample is assigned only one label and cannot be assigned more than one label at a time. For example, an image can be classified as that of a cat, dog, or rabbit, and not more than one of them at the same time.
Multilabel classification: In the case...
