[scikit-learn] Confidence Estimation for Regressor Predictions

[Jiří Fejfar]

Maybe you can also use bootstrap method published by Efron? You can see
https://web.stanford.edu/~hastie/local.ftp/Springer/OLD/ESLII_print4.pdf

It is implemented in resampling module with replacement option, if I can
understand.

J.

Dne 1.9.2016 21:46 napsal uživatel "Roman Yurchak" <rth.yurchak at gmail.com>:

> I'm also interested to know if there are any projects similar to
> scikit-learn-contrib/forest-confidence-interval for linear_model or SVM
> regressors.
>
> In the general case, I think you could get a quick first order
> approximation of the confidence interval for your regressor, if you take
> the standard deviation  of predictions obtained by fitting different
> subsets of your data using,
>      cross_validation.cross_val_score( ).std()
> with a fixed set of estimator parameters? Or some multiple of it (e.g.
> 2*std). Though this will probably not match exactly the mathematical
> definition of a confidence interval.
> --
> Roman
>
>
> On 01/09/16 20:32, Dale T Smith wrote:
> > There is a scikit-learn-contrib project with confidence intervals for
> random forests.
> >
> > https://github.com/scikit-learn-contrib/forest-confidence-interval
> >
> >
> > ____________________________________________________________
> ______________________________
> > Dale Smith | Macy's Systems and Technology | IFS eCommerce | Data
> Science and Capacity Planning
> >  | 5985 State Bridge Road, Johns Creek, GA 30097 |
> dale.t.smith at macys.com
> >
> > -----Original Message-----
> > From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=
> macys.com at python.org] On Behalf Of Daniel Seeliger via scikit-learn
> > Sent: Thursday, September 1, 2016 2:28 PM
> > To: scikit-learn at python.org
> > Cc: Daniel Seeliger
> > Subject: [scikit-learn] Confidence Estimation for Regressor Predictions
> >
> > ⚠ EXT MSG:
> >
> > Dear all,
> >
> > For classifiers I make use of the predict_proba method to compute a Gini
> coefficient or entropy to get an estimate of how "sure" the model is about
> an individual prediction.
> >
> > Is there anything similar I could use for regression models? I guess for
> a RandomForest I could simply use the indiviual predictions of each tree in
> clf.estimators_ and compute a standard deviation but I guess this is not a
> generic approach I can use for other regressors like the
> GradientBoostingRegressor or a SVR.
> >
> > Thanks a lot for your help,
> > Daniel
> > _______________________________________________
> > scikit-learn mailing list
> > scikit-learn at python.org
> > https://mail.python.org/mailman/listinfo/scikit-learn
> >
> > * This is an EXTERNAL EMAIL. Stop and think before clicking a link or
> opening attachments.
> > _______________________________________________
> > scikit-learn mailing list
> > scikit-learn at python.org
> > https://mail.python.org/mailman/listinfo/scikit-learn
> >
>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20160901/167e72fc/attachment.html>