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
<mailto: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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