You start testing out the Naïve Bayes algorithm and you’ve found that the zero frequency problem is incredibly real with the “forever alone” pandas. There would be an enormous subset of pandas who will never have sex. Alternatively, the ones who will are expected to get real busy and produce a modest improvement in panda fertility. You could go on with the algorithm, or you could change up the priors to try and help out the “forever alone” pandas. What do you do.
Heavily weight your priors on the ability to play DnD, as nearly every classified “forever alone” panda had a propensity for roll playing games and not the sexy type of roll playing games where one panda’s a plumber and the other doesn’t know how to pay for their goods and services and doesn’t accept IOUs
B McGraw has lived a long and successful professional life as a software developer and researcher. After completing his BS in spaghetti coding at the department of the dark arts at Cranberry Lemon in 2005 he wasted no time in getting a masters in debugging by print statement in 2008 and obtaining his PhD with research in screwing up repos on Github in 2014. That's when he could finally get paid. In 2018 B McGraw finally made the big step of defaulting on his student loans and began advancing his career by adding his name on other people's research papers after finding one grammatical mistake in the Peer Review process.
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Evaluating a Naive Bayes model is important because a good-looking accuracy score does not always mean the classifier is making useful predictions. Looking at metrics such as precision, recall, and the types of errors being produced can provide a much clearer picture of how the model behaves in practice
Evaluating a Naive Bayes model is important because a good-looking accuracy score does not always mean the classifier is making useful predictions. Looking at metrics such as precision, recall, and the types of errors being produced can provide a much clearer picture of how the model behaves in practice