2014-03-19 21:40 GMT+01:00 Satrajit Ghosh :
> this would mean that any tree-based model could generate differences based
> on preprocessing differences right?
Yes. I'm not sure why the threshold is there, but it's probably to
prevent generating too many splits in the face of noisy input. A
cleaner
thanks lars.
this would mean that any tree-based model could generate differences based
on preprocessing differences right?
cheers,
satra
On Sun, Mar 16, 2014 at 3:37 PM, Olivier Grisel wrote:
> 2014-03-16 0:23 GMT+01:00 Lars Buitinck :
> > 2014-03-15 21:53 GMT+01:00 Satrajit Ghosh :
> >> in m
2014-03-16 0:23 GMT+01:00 Lars Buitinck :
> 2014-03-15 21:53 GMT+01:00 Satrajit Ghosh :
>> in many cases with fat data (small samples<50 x many features>10) i have
>> found that standardizing helps quite a bit in case of extra trees. i still
>> don't have a good understanding as to why this is
2014-03-15 21:53 GMT+01:00 Satrajit Ghosh :
> in many cases with fat data (small samples<50 x many features>10) i have
> found that standardizing helps quite a bit in case of extra trees. i still
> don't have a good understanding as to why this is the case. it could simply
> be small sample bia
thanks gilles,
that makes sense. i haven't checked random forest classification on these
data. i'll check that as well.
cheers,
satra
On Sat, Mar 15, 2014 at 5:51 PM, Gilles Louppe wrote:
> Hi Satra,
>
> In case of Extra-Trees, changing the scale of features might change
> the result when th
Hi Satra,
In case of Extra-Trees, changing the scale of features might change
the result when the transform you apply distorts the original feature
space. Drawing a threshold uniformly at random in the original
[min;max] interval won't be equivalent to drawing a threshold in
[f(min);f(max)] if f i
hi olivier,
just a question on this statement:
Random Forest (and decision tree-based models in general) are scale
> independent.
>
in many cases with fat data (small samples<50 x many features>10) i
have found that standardizing helps quite a bit in case of extra trees. i
still don't have a
Thanks a lot for this detailed answer!
Kind regards,
Kevin
Le 14/03/2014 16:37, Olivier Grisel a écrit :
> 2014-03-14 15:34 GMT+01:00 Kevin Keraudren :
>> Hi,
>>
>> I have a question related to the range of my input data for SVM or
>> Random Forests for classification:
>> I normalise my input vec
2014-03-14 15:34 GMT+01:00 Kevin Keraudren :
> Hi,
>
> I have a question related to the range of my input data for SVM or
> Random Forests for classification:
> I normalise my input vectors so that their euclidean norm is one, for
> instance to limit the influence of the image size or intensity con
Hi,
I have a question related to the range of my input data for SVM or
Random Forests for classification:
I normalise my input vectors so that their euclidean norm is one, for
instance to limit the influence of the image size or intensity contrast.
I took the habit of then scaling them, multipl
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