Wow, good thing you decided to clean out all that. You found some real problematic artifacts in there. Some doofus put the timestamps for the panda birthdays in excel and you began optimizing on one hundred year old pandas before you found the mistake! You’ve taken all your entire feature space and made one kick-ass Naïve Bayese classifier. Not only do you have classifications for each Panda’s sexual type but you know have an estimated probability of each panda being a particular type. Unfortunately, there is an issue…in your dataset, there are a particular subset of pandas who have never gotten lucky. Your Naïve Bayes has classified them as “Forever alone” due to not eating enough bamboo and not being as monstrously jacked as other pandas and/or having personality issues. Your algorithm assigned them a zero probability of ever getting down to pound town! Many of the other panda classifiers are adequately paired up with an expected value of four panda cubs per pair. Maybe the algorithm’s right and those forever alone pandas are doomed, maybe they just need a chance. What do you do?
Good enough…That’s a good expected value of panda cubs
Evaluate it with a testing dataset, maybe they’re just asexual or something?
Use some ole Fashioned Regression