@peter, yup you can :D , also if you were looking at svms you can generate
probabilities for those as well.
On Tue, Jul 22, 2014 at 9:32 PM, Mathieu Blondel <math...@mblondel.org>
wrote:
>
>
>
> On Wed, Jul 23, 2014 at 4:47 AM, Peter Prettenhofer <
> peter.prettenho...@gmail.com> wrote:
>
>>
>> An alternative is to use a GradientBoostingRegressor with quantile loss
>> to generate prediction intervals (see [1]) -- only for the keen - i've once
>> used that unsuccessfully in a Kaggle comp. Its not a confidence score
>> though -- it can only tell you if its within a band.
>>
>
> Indeed, the notion of quantile regression seems to differ from confidence
> intervals. After all, we could also ask for a confidence interval on the
> quantile predictions.
>
> Besides my PR as mentioned by Peter, there is also this issue for RF and
> bagging estimators
> https://github.com/scikit-learn/scikit-learn/issues/3271
>
> For these estimators, it is trivial to compute the empirical variances.
> # I was kind of hoping someone would implement it during the spring ;-)
>
> It will be nice to have have prediction variances in several estimators
> and with a consistent API.
>
> Mathieu
>
>
>
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