Hi, Thank you for the reply. Please bear with me for a while.
>From where did this number, 0.632, come? I have no background in statistics (which appears to be the case here!). Or let me rephrase my query: what is this bootstrap sampling all about? Searched the web, but didn't get satisfactory results. Thanks On Tue, Oct 4, 2016 at 12:02 AM, Sebastian Raschka <se.rasc...@gmail.com> wrote: > > From whatever little knowledge I gained last night about Random Forests, > each tree is trained with a sub-sample of original dataset (usually with > replacement)?. > > Yes, that should be correct! > > > Now, what I am not able to understand is - if entire dataset is used to > train each of the trees, then how does the classifier estimates the OOB > error? None of the entries of the dataset is an oob for any of the trees. > (Pardon me if all this sounds BS) > > If you take an n-size bootstrap sample, where n is the number of samples > in your dataset, you have asymptotically 0.632 * n unique samples in your > bootstrap set. Or in other words 0.368 * n samples are not used for growing > the respective tree (to compute the OOB). As far as I understand, the > random forest OOB score is then computed as the average OOB of each tee > (correct me if I am wrong!). > > Best, > Sebastian > > > On Oct 3, 2016, at 2:25 PM, Ibrahim Dalal via scikit-learn < > scikit-learn@python.org> wrote: > > > > Dear Developers, > > > > From whatever little knowledge I gained last night about Random Forests, > each tree is trained with a sub-sample of original dataset (usually with > replacement)?. > > > > (Note: Please do correct me if I am not making any sense.) > > > > RandomForestClassifier has an option of 'bootstrap'. The API states the > following > > > > The sub-sample size is always the same as the original input sample size > but the samples are drawn with replacement if bootstrap=True (default). > > > > Now, what I am not able to understand is - if entire dataset is used to > train each of the trees, then how does the classifier estimates the OOB > error? None of the entries of the dataset is an oob for any of the trees. > (Pardon me if all this sounds BS) > > > > Help this mere mortal. > > > > Thanks > > _______________________________________________ > > scikit-learn mailing list > > scikit-learn@python.org > > https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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