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 [[alternative HTML version deleted]]