I'm working on a naive Bayes classifier that calculates probabilities using a normal Gaussian distribution. This works very well when I am classifying something into two mutually exclusive buckets (e.g. spam vs. not-spam), but when I am working with a factor that is not easily classified that way (when the classifications are not mutually exclusive) I would like to express the result as a percentage.
When I combine the probability density of several factors (by multiplying them together) I tend to get a very small number and I would like to adjust that so I can express it in a 0-100 percent range, so it will be more easily understood. Is there another factor I can use to adjust the probability density into a percentage range?
For example: in the Wikipedia article for naive Bayes classifiers, there is an example of using height, weight and shoe size variables to classify a person as male or female. After computing the numbers, the posterior result for the female classification is 5.3778E-4 (or .00053778). Out of context that seems minuscule, but if you compare that number to the result for the male classification, the percentage would be 99.99% female. What factors (if any) could I apply to the posterior result to get that percentage, without the benefit of the male result to compare it to?