Comment by jstults on Two Challenges · 2010-02-17T19:29:38.917Z · LW · GW

On the basis of these remarks I submit the following qualified statement: while the belief network paradigm is mathematically elegant and intuitively appealing, it is NOT very useful for describing real data.

The challenge is just as wrong; to quote from the wiki:

Classifier performance depends greatly on the characteristics of the data to be classified. There is no single classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem. Determining a suitable classifier for a given problem is still more an art than science.

Russell and Norvig, 1st ed. has a good example comparing the performance of a Bayes net with a decision tree on data that was generated by a decision tree-like process, of course the net did not perform as well as a decision tree on that data, surprise, surprise.