Hi!
I'm using the Naive Bayes classifier provided by the e1071 package (
http://cran.r-project.org/web/packages/e1071) and I've noticed that the
predict function has a different behavior when the level set of the columns
used for prediction is different from the ones used for fitting. From
inspecting the predict.naiveBayes I came to the conclusion that this is due
to the conversion of factors to their internal codes using the data.matrix
function. For example, consider the following piece of R code:
> library(mlbench)
> library(e1071)
> data(HouseVotes84)
> model <- naiveBayes(Class ~ ., data = HouseVotes84)
> head(HouseVotes84)
Class V1 V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 V12 V13 V14 V15 V16
1 republican n y n y y y n n n y <NA> y y y n y
2 republican n y n y y y n n n n n y y y n <NA>
3 democrat <NA> y y <NA> y y n n n n y n y y n n
4 democrat n y y n <NA> y n n n n y n y n n y
5 democrat y y y n y y n n n n y <NA> y y y y
6 democrat n y y n y y n n n n n n y y y y
> predict(model, HouseVotes84[1,-1])
[1] republican
Levels: democrat republican
> new.data <- data.frame(V1="n", V2="y", V3="n", V4="y", V5="y", V6="y",
V7="n", V8="n", V9="n", V10="y", V11=NA_character_, V12="y", V13="y",
V14="y", V15="n", V16="y", stringsAsFactors=TRUE)
> predict(model, new.data)
[1] democrat
Levels: democrat republican
I haven't used other classification methods in R, so I'm unsure if this is
what is expected from the application of the predict function. Is this a
bug or the expected behavior?
Thanks!
--
Joao.
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