How do you perform multilabel classification that is also a multiclass problem? I have a data set in which each row of data belongs to certain classes/labels.




text
class1
class2
class3




text1
pos
neg
na


text2
na
neg
na


text3
na
neu
na


text4
pos
neg
neg


text5
neg
neg
na




There are basically 4 classes with 3 labels each (pos, neg, neu, na). I suppose this is both a multiclass and multilabel problem. How do I approach this? I am using the BinaryRelevance function from multisklearn but the result always returns 2 classes only (0 and 1). What is the correct way to do this?
From the description this is not a multilabel problem because:

Each of the three &quot;classes&quot; (columns) must have a label. In a multilabel problem every class is optional.
Every &quot;class&quot; (column) appears to have a specific purpose subdivided into 4 labels. In a regular multilabel problem the labels are exchangeable, e.g. a document can have topics &quot;sports&quot; and &quot;society&quot; but not &quot;politics&quot;, all of these labels have no order and no specific role distinct from the others.

It seems that you simply have three regular independent multiclass problems:

problem 1 = predict &quot;class 1&quot;
problem 2 = predict &quot;class 2&quot;
problem 3 = predict &quot;class 3&quot;

Note: the word &quot;class&quot; for the columns is confusing because these don't correspond to the regular concept of class.