Dear all, this is not a pure R question, but really about how to set up a
multinomial logistic regression model to do a multi-class classification. I
would really appreciate if any of you would give me some of your thoughts and
recommendation.
Let's say we have 3-class classification problem: A, B and C. I have certain
number of samples, with each sample, I have 3 variables (Xa, Xb and Xc). The
trick here is that these 3 variables measures the extent of the likelihood of
the samples being class A, B and C, i.e., Xa for class A, Xb for class B and Xc
for class C. For a given sample i, we can simply make a rough prediction based
on the values of Xa, Xb and Xc. For example:
for sample 1, Xa=10, Xb=50, Xc=15, then most likely I would predict sample 1 as
class "B".
Then I have another set of variables Ya, Yb and Yc doing similar things.
I can construct a dataset as below:
Xa Xb Xc Ya Yb Yc class
sample 1 10 50 15 0.2 0.8 0.1 B
sample 2 8 4 6 0.7 0.5 0.3 A
:
:
and then make a model fit<-multinom(class~Xa+Xb+Xc+Ya+Yb+Yc)
But my understanding is that this model is not working in a way of by simply
looking at each row of the data and pick the class that has the best Xs and/or
Ys. In leave-one-out, sometimes it picks up a class that apparently is not a
winner if I compare across Xs and Ys.
Greatly appreciate if anyone can suggest a more sensible way to construct the
data and/or a different way of thinking of the problem at all.
John
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