I got ExtraTreesRegressor running on IPython.parallel (Pyrallel doesn’t work
for me but the example at
http://nbviewer.ipython.org/github/ogrisel/notebooks/blob/master/Distributed%20Learning%20of%20Extra%20Trees%20with%20IPython.parallel.ipynbdid).
Now I’d like to be able to predict my error (i.e provide a confidence
interval?) It doesn’t look like ExtraTreesRegressor provides that, so I thought
I might try training a second model to predict the error.
1. Is this crazy and/or stupid? I’m using the same factors to predict the
error as I am to predict the result. I’m afraid there might be a circularity
there but I can’t see it.
2. ExtraTreesRegressor is too good! Even if I train on half, my median error
is .025%. I mostly care if the error is more than 8% but those cases are so
rare, I can’t really train on it. I could set my threshold to 0.1% but that is
far too strict for my purposes.
I’m actually a little worried that my ExtraTreesRegressor is too good to be
true. But I can't see anything wrong with my cross validation.
A little more background in case it helps:
I am trying to extrapolate to unknown data. The known dataset is not
representative of the unknown (i.e. it’s skewed) which is why predicting the
error is important. In other words, my model needs to know when it’s
encountering a situation it doesn’t know enough about.
Thanks in advance,
Alessandro Gagliardi| Glassdoor|
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