Hi Jason,

It looks like you are evaluating your error on your training data,
aren't you? It will give you a (very) poor estimate of the
generalization error of your model. Instead, try your model on an
independent part of your dataset (in particular, one which has a not
been used to fit to your forest), it should give you a better
estimate. You can also evaluate your model within a cross-validation
loop.

Best,

Gilles

On 15 August 2013 09:12, Robert Layton <[email protected]> wrote:
> The first thing I'd do is publish the result (just kidding!).
>
> Try it with another data set first, especially one that has an example in
> the docs.
> If you are still getting top marks, it may be your "framework" around the
> code. (are you doing proper test/train splits, etc)
> If it drops, consider that you may have a dataset that can get high
> accuracies. Random Forests are good methods...
>
>
> On 15 August 2013 17:03, Jason Williams <[email protected]> wrote:
>>
>> I ran a few test based on Random Forest Classifier
>> (http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html)
>> with default setting. The classification (repeated the classification
>> procedure several times) is nearly 100% correct. That seems to be
>> overfitting. Is there any points (e.g. dataset, etc.) I can check to see if
>> I did something wrong?
>>
>> Thanks
>>
>>
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>
>
>
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