Darren DeRidder / @73rhodes
naive bayesian classifiers
Computer Systems Engineer
Real-time • AAA • Network Security • Mobile
Tech lead on Kindsight Mobile Security @ Alcatel
Mobile World Congress • Blackhat 2013
"I Am Not A Data Scientist"
and that's ok!
There are lots of tools available for us mortals.
simple, yet surprisingly effective
` P(A|B) = (P(B|A)P(A)) / (P(B)) = ...`
`= (P(B|A)P(A)) / ( P(B|A) P(A) + (1-P(B|A))(1-P(A)))`
`P(A) = ( prod_(i=1)^n P(A|W_i) ) / ( (prod_(i=1)^n P(A|W_i)) + (prod_(i=1)^n (1 - P(A|W_i))) )`
Or, in Plain English
a box of chocolates.
You never know what you're gonna get.
(But you can make a pretty good guess!)
What if we pick a round, light chocolate?
A round, light chocolate...
`x = 0.225 / 0.075 = 3`
A round, light chocolate is 3 times more likely to have nuts.
(This is a likelihood function.)
Classify as "Nuts" or "No Nuts", with some level of certainty.
`P(N) = 0.225 / (0.225 + 0.075) = 0.75 = 75%`
(We're 75% sure this chocolate has nuts.)
Optimized binary classifier for limited vocabularies.
Leverages "missing" traits to improve accuracy by ~10%.
Used in production...