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For linear regression, one can calculate a confidence interval for a prediction. Is this possible for all machine learning techniques?

For example let's say I fit a neural network, and then start using it to make predictions on new data. Is it possible to create a confidence interval around my prediction? Perhaps using the distribution of the cross validation errors? Or perhaps using an entirely new machine learning model to predict the error?

I'm really try to wrap my head around all of this. I really appreciate any insight.

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closed as too broad by kjetil b halvorsen, Michael Chernick, Peter Flom Apr 25 at 12:22

Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer. Avoid asking multiple distinct questions at once. See the How to Ask page for help clarifying this question. If this question can be reworded to fit the rules in the help center, please edit the question.

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    $\begingroup$ Confidence interval requires a population statistic and a sample size. If you have these two things, you can build a confidence interval. BTW, some might bristle at regression being called a machine learning technique. $\endgroup$ – mandata Aug 11 '15 at 16:51