Is it possible to use both kinds (nominal, numerical) of features in designing training set for Naive byes machine learning algorithm. Can we mention feature frequencies for designing feature vectors in Naive Byes machine learning algorithm. when Features are represented in Byes algorithm, which out of binary(term x and y are represented by 0,1) and TF (term geography and chemistry are represented by 10,20) approach is better for classifying large size documents.
Yes, Zafar.
You can write combined likelihood, estimate parameters by likelihood maximization and put the estimates into likelihood ratio for classification.
I think it's enough.
Hello,
In any statistical estimation problem where probability theory applies, you can form joint densities for discrete and continuous random variables. This applies to machine learning algorithms also. For a Bayes' classifier, you are casting the problem as a hypothesis test on a mixed space of discrete and continuous RVs. Since you wish to perform classification rather than estimation, you need to treat the continuous variables as a discrete class. You can do this by testing their membership with respect to a set, e.g. like thresholding. Alternatively you can apply a generalised likelihood ratio test where you maximise the likelihood over the continuous random variables. This leaves you with a likelihood function that has discrete variables.
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