bayesian-networks

I am doing some research about different data mining techniques and came across something that I could not figure out. If any one have any idea that would be great. In which cases is it better to use a Decision tree and other cases a Naive Bayes classifier? Why use one of them in certain cases? And the other in different cases? (By looking at its functionality, not at the algorithm) Anyone have some explanations or references about this? Decision Trees are very flexible, easy to understand, and easy to debug. They will work with classification problems and regression problems. So if you are

I'm using Bayesian Network Repository ( http://www.bnlearn.com/bnrepository/gaussian-medium.html#ecoli70 ) to obtain several datasets. To learn a Bayesian network structure, I need the observational data from each dataset. I'm not sure if Bayesian Network Repository includes this information or not. If it does, my question is how I can extract observational data from the datasets given in Bayesian Network Repository dataset. The observational data is in the form of a matrix of size n by p where n is the number of samples and p is the number of random variables. I think you're looking for the

What is the difference between a Bayesian network and a Naive Bayes classifier? I noticed one is just implemented in Matlab as classify the other has an entire net toolbox. If you could explain in your answer which one is more likely to provide a better accuracy as well I would be grateful (not a pre-requisite). Richante Short answer, if you're only interested in solving a prediction task: use Naive Bayes. A Bayesian network (has a good wikipedia page) models relationships between features in a very general way. If you know what these relationships are, or have enough data to derive them, then