Hi, Raga, I have a short section on this here (https://sebastianraschka.com/blog/2016/model-evaluation-selection-part2.html#the-bootstrap-method-and-empirical-confidence-intervals) if it helps.
Best, Sebastian > On Mar 1, 2017, at 3:07 PM, Raga Markely <raga.mark...@gmail.com> wrote: > > Hi everyone, > > I wonder if you could provide me with some suggestions on how to determine > the confidence and prediction intervals of SVR? If you have suggestions for > any machine learning algorithms in general, that would be fine too (doesn't > have to be specific for SVR). > > So far, I have found: > 1. Bootstrap: > http://stats.stackexchange.com/questions/183230/bootstrapping-confidence-interval-from-a-regression-prediction > 2. > http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0048723&type=printable > 3. ftp://ftp.esat.kuleuven.ac.be/sista/suykens/reports/10_156_v0.pdf > > But, I don't fully understand the details in #2 and #3 to the point that I > can write a step by step code. If I use bootstrap method, I can get the > confidence interval as follows? > a. Draw bootstrap sample of size n > b. Fit the SVR model (with hyperparameters chosen during model selection with > grid search cv) to this bootstrap sample > c. Use this model to predict the output variable y* from input variable X* > d. Repeat step a-c for, for instance, 100 times > e. Order the 100 values of y*, and determine, for instance, the 10th > percentile and 90th percentile (if we are looking for 0.8 confidence interval) > f. Repeat a-e for different values of X* to plot the prediction with > confidence interval > > But, I don't know how to get the prediction interval from here. > > Thank you very much, > Raga > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
