I was expecting there to be the actual poisson loss implemented in the
class, not just a log transform.
On 07/28/2015 02:03 PM, josef.p...@gmail.com wrote:
Just a comment from the statistics sidelines
taking log of target and fitting a linear or other model doesn't make
it into a Poisson model.
But maybe "Poisson loss" in machine learning is unrelated to the
Poisson distribution or a Poisson model with E(y| x) = exp(x beta). ?
Josef
On Tue, Jul 28, 2015 at 2:46 PM, Andreas Mueller <t3k...@gmail.com
<mailto:t3k...@gmail.com>> wrote:
I'd be happy with adding Poisson loss to more models, thought I
think it would be more natural to first add it to GLM before GBM ;)
If the addition is straight-forward, I think it would be a nice
contribution nevertheless.
1) for the user to do np.exp(gbmpoisson.predict(X)) is not
acceptable. This needs to be automatic. It would be best if this
could be done in a minimally intrusive way.
2) I'm not sure, maybe Peter can comment?
3) I would rather contribute sooner, but other might thing
differently. Silently ignoring sample weights is not an option,
but you can error if they are provided.
Hth,
Andy
On 07/23/2015 08:52 PM, Peter Rickwood wrote:
Hello sklearn developers,
I'd like the GBM implementation in sklearn to support Poisson
loss, and I'm comfortable in writing the code (I have modified my
local sklearn source already and am using Poisson loss GBM's).
The sklearn site says to get in touch via this list before making
a contribution, so is it worth me to submitting something along
these lines?
If the answer is yes, some quick questions:
1) The simplest implementation of poisson loss GBMs is to work in
log-space (i.e. the GBM predicts log(target) rather than target),
and require the user to then take the exponential of those
predictions. So, you would need to do something like:
gbmpoisson =
sklearn.ensemble.GradientBoostingRegressor(...)
gbmpoisson.fit(X,y)
preds = np.exp(predict(X))
I am comfortable making changes to the source for this to work,
but I'm not comfortable changing any of the higher-level
interface to deal automatically with the transform. In other
words, other developers would need to either be OK with the GBM
returning transformed predictions in the case where "poisson"
loss is chosen, or would need to change code in the 'predict'
function to automatically do the transformation is poisson loss
was specified. Is this OK?
2) If I do contribute, can you advise what the best tests are to
test/validate GBM loss functions before they are considered to
'work'?
3) Allowing for weighted samples is in theory easy enough to
implement, but is not something I have implemented yet. Is it
better to contribute code sooner that doesn't handle weighting
(i.e. just ignores sample weights), or later that does?
Cheers, and thanks for all your work on sklearn. Fantastic
tool/library,
Peter
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