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
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