[R] Error: Can not handle categorical predictors with more th an 32 categories.
Liaw, Andy
andy_liaw at merck.com
Wed Mar 23 01:25:51 CET 2005
It always helps to check whether you got the data into R correctly. Hint:
What does str(credit) tell you?
Andy
> From: Melanie Vida
>
> Hi All,
>
> My question is in regards to an error generated when using
> randomForest
> in R. Is there a special way to format the data in order to
> avoid this
> error, or am I completely confused on what the error implies?
>
> "Error in randomForest.default(m, y, ...) :
> Can not handle categorical predictors with more than
> 32 categories."
>
> This is generated from the command line:
> > credit.rf <- randomForest(V16 ~ ., data=credit, mtry=2,
> importance =
> TRUE, do.trace=100)
>
> The data set is the credit-screening data from the UCI respository,
> ftp://ftp.ics.uci.edu/pub/machine-learning-databases/credit-sc
reening/crx.data.
This data consists of 690 samples and 16 attributes.
The attribute information includes:
A1: b, a.
A2: continuous.
A3: continuous.
A4: u, y, l, t.
A5: g, p, gg.
A6: c, d, cc, i, j, k, m, r, q, w, x, e, aa, ff.
A7: v, h, bb, j, n, z, dd, ff, o.
A8: continuous.
A9: t, f.
A10: t, f.
A11: continuous.
A12: t, f.
A13: g, p, s.
A14: continuous.
A15: continuous.
A16: +,- (class attribute)
Has anyone tried randomForests in R on the credit-screening data set
from the UCI repository?
Thanks in advance for any useful hints and tips,
Melanie
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