Dale, I meant for all the methods in scikit.linear_model. Linear regression is well known, but say for rigde regression that does not look that simple http://stats.stackexchange.com/a/15417 . Thanks for mentioning the bootstrap method!
-- Roman On 01/09/16 21:55, Dale T Smith wrote: > Confidence intervals for linear models are well known - see any statistics > book or look it up on Wikipedia. You should be able to calculate everything > you need for a linear model just from the information the estimator provides. > Note the Rsquared provided by linear_model appears to be what statisticians > call the adjusted-Rsquared. > > > __________________________________________________________________________________________ > Dale Smith | Macy's Systems and Technology | IFS eCommerce | Data Science and > Capacity Planning > | 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.sm...@macys.com > > > -----Original Message----- > From: scikit-learn > [mailto:scikit-learn-bounces+dale.t.smith=macys....@python.org] On Behalf Of > Roman Yurchak > Sent: Thursday, September 1, 2016 3:45 PM > To: Scikit-learn user and developer mailing list > Subject: Re: [scikit-learn] Confidence Estimation for Regressor Predictions > > ⚠ EXT MSG: > > 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.sm...@macys.com >> >> -----Original Message----- >> From: scikit-learn >> [mailto:scikit-learn-bounces+dale.t.smith=macys....@python.org] On Behalf Of >> Daniel Seeliger via scikit-learn >> Sent: Thursday, September 1, 2016 2:28 PM >> To: scikit-learn@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@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@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 > > * This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening > attachments. > _______________________________________________ > 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
