Very low probability in naive Bayes classifier I have designed NB classifier from scratch in python for binary classification problem. There are total 220 records out of which 85 records belongs to 'Yes' class and 135 to 'No' class. My classifier is giving 88% accuracy.

So, whenever I calculate the posterior probability of one sample that belongs to 'Yes' class it is very low in terms of numbers. For eg., I'm making a prediction that whether the batsman is rising star (i.e. the probability of sample belongs to 'Yes' class ). 

Here The posterior probability of being rising star i.e. P(RS) is very low in numbers something like 2.33E-8. But the posterior probability of being not rising star is also very low similar in the range of E-8 to E-16. Some features I'm using to calculate posterior probability are also small values in the range of 0.1 to 0.01.

My question is how to represent posterior probability i.e. P(RS) in terms of percentage. Like P(RS)=90%.

PS: I googled this problem and tried log method which returns a negative value.
I can't tell you for sure without you describing your calculation more or showing code, but my guess is you're not actually calculating the posterior probability here. I bet this is just the conditional likelihood, or at best the unnormalized posterior. Remember: the posterior calculation has a division component. Does yours? You're probably forgetting to divide by the "evidence".