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
