I'm testing for a condition that happens about 25% of the time in the wild. I have 26 factors which can easily be measured and are relivant to the condition. The 26 factors (Fa through Fz) are distributed over their respective ranges (Famin -> Famax, Fbmin -> Fbmax...). I partition these ranges into buckets (115 has experimently proven to be an optimal number of buckets) and I measure the probability that each bucket results in the condition of interest. Then I sample the Fa-z for an unknown sample and use Bayes theorm to combine the probabilities for each bucket to forcast if the unknown sample will have the condition of interest. I'm getting about 70% accuracy in my forcasts, but I'd like to improve that, if possible.
I have two questions.
First, currently I break every factor into 115 buckets. Is there a mathmatical way to find the optimal number of buckets, possibly based on Standard Deviation or some other statistical analysis? I have the feeling that overall results would improve if Fa has a different number of buckets than Fb, etc.
Secondly, intuitively I expect that some factors have a higher correlation to the forcast than others, or (better yet) that if Fa has some condition, then Fb is more relavant, but if Fa is in another condition Fb could be ignored entirely. How could one determine these relationships? How does Bayes therom allow for "relevance"? Is it done with liniar coeficients or by exponential ones?