I did this:
nb <- naiveBayes(users, platform)
pl <- predict(nb,users)
nrow(users) ==> 314781
ncol(users) ==> 109
1. naiveBayes() was quite fast (~20 seconds), while predict() was slow
(tens of minutes). why?
2. the predict results were completely off the mark (quite the opposite
of the expected overfitting). suffice it to show the tables:
pl:
android blackberry ipad iphone lg linux mac
3 5 11 14 312723 5 11
mobile nokia samsung symbian unknown windows
1864 17 16 112 0 0
platform:
android blackberry ipad iphone lg linux mac
18013 1221 2647 1328 4 2936 34336
mobile nokia samsung symbian unknown windows
18 88 39 103 2660 251388
i.e., nb classified nearly everything as "lg" while in the actual data
"lg" is virtually nonexistent.
3. when I print "nb", I see "A-priori probabilities" (which are what I
expected) and "Conditional probabilities" which are confusing because
there are only two of them, e.g.:
android 0.048464998 0.43946764
blackberry 0.001638002 0.04045564
ipad 0.322251606 1.84940588
iphone 0.030873494 0.23250250
lg 0.000000000 0.00000000
linux 0.023501362 0.34698919
mac 0.082653774 1.22535027
mobile 0.000000000 0.00000000
nokia 0.000000000 0.00000000
samsung 0.000000000 0.00000000
symbian 0.000000000 0.00000000
unknown 0.003759398 0.08219078
windows 0.021158528 0.32916970
the predictors are integers.
is the first column for the 0 predictors and the second for all non-0?
Is there a way to ask naiveBayes to differenciate between non-0 values?
thanks!
--
Sam Steingold (http://sds.podval.org/) on Ubuntu 11.10 (oneiric) X 11.0.11004000
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The program isn't debugged until the last user is dead.
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