[klaR package] [NaiveBayes] warning message numerical 0 probability

Uwe Ligges<ligges<at>  statistik.tu-dortmund.de>  writes:

On 03.11.2010 16:26, Fabon Dzogang wrote:

Hi,

I run R 2.10.1 under ubuntu 10.04 LTS (Lucid Lynx) and klaR version 0.6-4.

I compute a model over a 2 classes dataset (composed of 700 examples).
To that aim, I use the function NaiveBayes provided in the package
klaR.
When I then use the prediction function : predict(my_model, new_data).
I get the following warning :

"In FUN(1:747[[747L]], ...) : Numerical 0 probability with observation 458"

As I did not find any documentation or any discussion concerning this
warning message, I looked in the klaR source code and found the
following line in predict.NaiveBayes.R :

"warning("Numerical 0 probability with observation ", i)"

Within Naive Bayes in order to calculate the posteriori probabilities of
the classes it is necessary to calculate the probabilities of the
observations given the classes. The function NaiveBayes prints a warning
if all these probabilities are numerical 0, i.e. that the observation
has a numerical probability of 0 for *all* classes. Usually this is only
the case when the obs. is an extreme outlier.

I will change the warning to say "all classes" in further releases of klaR.

Best wishes,
Uwe Ligges

Unfortunately, it is hard to get a clear picture of the whole process
reading the code. I wonder if someone could help me with the meaning
of this warning message.

Sorry I did not provide an example, but I could not simulate the same
message over a small toy example.

Thank you,

Fabon Dzogang.

Hello,

I am following this thread after it's one year old!

I am also getting the warnings for probabilities=0, but for ALL observations!
I am using NaiveBayes() to obtain the posteriors of all training examples (29
classes; 49 numeric continuous variables, 700 examples), thus the command:

my.model<-NaiveBayes(class ~ ., data = data, usekernel = TRUE)

predict(my.model)

as expected, it returns predicted classes and a matrix of posteriors for each
training examples, BUT with this warning for ALL training examples. Still the
accuracy is ~60% and is within expectation.
Please give some comments, Is this possible?? Should I ignore the warnings??

The posteriors returned look like this:

          Class1       class2        class3        class4  ...
1  6.725408e-58  0.999999996  4.277379e-09  2.923033e-90
2  4.443767e-48  0.997316780  2.683220e-03  1.710466e-75
3  3.348223e-40  0.999993916  6.799327e-08  6.760083e-81
... ...

Sorry I am replying via the Gmane interface and cannot attach my dataset. But I
am starting to follow this mailing list now, so may be able to reply with
attachments to the next message.

grateful for any comments!

Ted Chang