I am not statistician, and I have a simple question.
When is better to use classification and when is better to use multinomial logistic regression?
"Classification" is a broad class of models/methods. Multinomial logistic regression is one model in that class. Are you looking to build a classifier to predict future responses or to explain factors associated with different responses? If the former and you have lots of predictors there are probably better methods out there, like random forest or SVM (different methods handle imbalances differently, some better than others). If you are interested in makings statements about how the independent factors (what is the 938, 11, and 7 in your example?) are associated with the classes then multinomial regression will give you statistics about each of them that have a plain English interpretation. Multinomial regression is robust to imbalance as long as you have sufficient numbers in each response group/class (ie > 10).
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