Apology for posting almost one question daily. I am trying to learn some aspects of Statistical Machine learning, so every day many questions coming and if I am not finding answer in my offline peer community, I am trying to ask to you.
You answer so nicely it encourages people like me to ask.
I was trying to go around Naive Bayes model. It seems it supports Multi-class classification.
Now there are some binary classifiers also like logistic regression.
I was thinking how would be the difference of estimation between binary classification
and multiclass classification.( not multinomial logistic or multilabel).
If any one of the esteemed members of the group may kindly absolve my query.
Thanking you in Advance,
Regards,
Subhabrata Banerjee.
Best Answer
My advice is first to try at least to search on Internet. The wikipedia page for Multiclass classification explains in clear terms what it means, and it is not hard to find it after a search for "multiclass classification".
A multi-class classifier is able to classify into more 2 outcomes (classes). It is a synonym with multinomial classification. Thus, multinomial logistic regression is a multi-class classification.
However, multi-label classification it is not. Multi-label classification assumes that one observation can be labeled with (classified as) more than one category/label/class, while multi-class does not (only one class allowed for an instance).