So I think it should be as
# for n_estimators=10
clf = RandomForestClassifier(n_estimators=10, oob_score=True)
clf.fit(X,y)
print clf.oob_score_
clf.oob_score_ will give oob accuracy.
But I would also like to know what percent of data is used to calculate
this score?
On Wed, Sep 12, 2012 at 1:14 PM, Brian Holt <[email protected]> wrote:
> You're absolutely right, you can simply use the oob estimate as your
> measure of generalisability. No need for GridSearchCV...
> On Sep 12, 2012 12:09 PM, "Sheila the angel" <[email protected]>
> wrote:
>
>> Hello all,
>> I want to optimize n_estimators and max_features for ensemble
>> methods (say forRandomForestClassifier ).
>> Usually I use GridSearchCV() with cv=4 which do 4 fold cross validation
>> for data and gives best parameter/model.
>> In the document section 'out-of-bag-estimates'
>> http://scikit-learn.org/dev/modules/grid_search.html#out-of-bag-estimates
>> says 'left out portion can be used to estimate the generalization error
>> without having to rely on a separate validation set'.
>>
>> 'out-of-bag-estimates' suggests GridSearchCV() is not needed in this
>> case!!
>> If so how do I get best parameter values and model.
>> Or I got the whole concept wrong?
>>
>> Thanks.
>> --
>> Sheila
>>
>>
>>
>>
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