How do you treat multi-class classification use case?

Question

I have a list of labelled text. Some have one label, others have 2 and some have even 3. Do you treat this as a multi-class classification problem?

Answer 1

The type of classification problem to solve depends on what your goal is, id don't know exactly what type of problem you are trying to solve, but from the form of data i presume you are talking about a multi-label classification problem.

In any case let's make some clarifications:

• Multi-class classification: you can have many classes (dog,cat,bear, ...) but each sample can be assigned only to one class, a dog cannot be a cat.

• Multi-label classfication the goal of this approach is assigning a set of labels to samples, in the text classification scenario for example the phrase "Today is the weather is sunny" may be assigned the set of labels ["weather","good"].

So, if you need to assign each sample to one class only, based on some metric that for example can be tied to the labels, you should use a multi-class algorithm,

but if your goal is predicting the labels that are most appropriate for your sample (text tagging for ex.), then we are talking about a multi-label classification problem.