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 | [email protected] -----Original Message----- From: scikit-learn [mailto:[email protected]] 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 | [email protected] > > -----Original Message----- > From: scikit-learn > [mailto:[email protected]] On Behalf Of > Daniel Seeliger via scikit-learn > Sent: Thursday, September 1, 2016 2:28 PM > To: [email protected] > 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 > [email protected] > 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 > [email protected] > https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ scikit-learn mailing list [email protected] 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 [email protected] https://mail.python.org/mailman/listinfo/scikit-learn
