[scikit-learn] Confidence Estimation for Regressor Predictions

[Dale T Smith]

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
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