Understanding Multiclass Classification
The classification algorithms that you have seen so far were mostly binary classifiers, where the target variable can have only two categorical values or classes. However, there can be scenarios where you 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 most suitable for a customer, such as cashback, air miles, gas station, or shopping. In scenarios such as these, where you have more than two classes, a slightly different approach is required compared to binary classification.
Multiclass classification problems can broadly be 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...
