[R] Error: Can not handle categorical predictors with more than 32 categories.

From: Melanie Vida <mvida_at_mitre.org>
Date: Wed 23 Mar 2005 - 10:14:09 EST


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-screening/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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