Hi,
I've got a feeling this has already been mentioned on the list before,
but can't find the relevant posts...
My question is, in a cross-validated training/testing and is there a
built-in way to access the whole list of individual predictions for each
sample, in the original sample order (rather getting the predictions
separately for each fold, and recombining them by hand)?
... such that float(sum(ds.targets==predictions))/len(ds.targets) would
give you the accuracy rate ...
And is there a standard way of getting the measure of confidence of the
classifier in each prediction (binary, in my current case). This must be
classifier specific, but I guess that GNB should give a probability for
each of it's classifications.
thanks for any tips!
Brian
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