thanks andy.
are there any general heuristics for these parameters - given that their
ranges are over the samples?
max_depth = range(1, nsamples)
or
min_samples_leaves = range(1, nsamples)
also related question: given that nsamples would actually depend on the cv
method of the GridSearchCV, is there a way to specify possible ranges
without trying to calculate what the CV method would do?
i.e is there a way to couple the parameter specification to when the grid
search runs the internal CV such that it will limit the parameter based on
the size of the internal training set?
cheers,
satra
On Sat, Sep 27, 2014 at 2:25 AM, Andy <t3k...@gmail.com> wrote:
> Hi Satra.
> You should set "n_estimators" as high as you can afford time and memory
> wise, and then cross-validate over (at least) one of the regularization
> parameters,
> for example over max_depth or min_samples_leaves. You can also search over
> max_features.
>
> Cheers,
> Andy
>
>
>
> On 09/26/2014 10:24 PM, Satrajit Ghosh wrote:
>
> hi folks,
>
> what are some useful ranges of parameters to throw into a grid search?
> and are there specific difference between randomforests and extra trees? i
> understand one could try different impurity measures for classification,
> but any suggestions on sensitivity of other parameters would be nice.
>
> cheers,
>
> satra
>
> On Thu, Sep 25, 2014 at 8:48 AM, Andy <t3k...@gmail.com> wrote:
>
>> On 09/23/2014 11:50 PM, Pagliari, Roberto wrote:
>>
>> I’m a bit confused as to why gridsearchCV is not needed with random
>> forests. I understand that with RF, each tree will only get to see a
>> partial representation of the data.
>>
>> Why do you say GridSearchCV is not needed?
>> I think it should always be used, only not for setting n_estimators.
>> You can use the oob estimates, but actually I don't think we have an
>> automated way to use these to adjust parameters.
>>
>>
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