I am using bagging to perform Bagged Regression Trees on count data (bird
abundance in Britain and Ireland, in relation to climate and land cover
variables). Predictions from the final model are visually believable but I
would really like a diagnostic equivalent to classification success that can be
used to decide if a model is adequate. Whereas with classification data an
error rate is returned, with continuous data only the root mean squared error
is returned. The RMSE is helpful for comparing different models for the same
species and deciding which is best, but as far as I can tell it offers no
absolute measure of how good that best model is.
At present I am using the final model to make predictions for the original
dataset and then computing a correlation coefficient between observed and
predicted values but I expect this is probably biased high due to
non-independence. Ideally I think I need the correlation coefficient between
the predictions and observed values for the out of bag sample for each of the n
trees produced, but I don't see this produced anywhere.
Does anyone know of a means of getting a useful unbiased diagnostic for
assessing overall fit?
thanks
Simon
____________________________________________________________
Sign-up for Bird Atlas 2007-11 at www.birdatlas.net
____________________________________________________________
Dr Simon Gillings
Senior Research Ecologist - Land Use
British Trust for Ornithology
The Nunnery, Thetford, Norfolk, IP24 2PU, UK
Tel +44(0)1842 750050 Fax +44(0)1842 750030
Charity No 216652 (England and Wales)
Company Limited by Guarantee No 357284 (England and Wales)
Registered Office The Nunnery, Thetford, Norfolk IP24 2PU
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