[scikit-learn] Confidence Estimation for Regressor Predictions

[Roman Yurchak]

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
>