Le 18 août 2014 09:57, "Arman Eshaghi" <[email protected]> a écrit : > > thanks for the discussion. Could you please what the right way of using boostraping for confidence interval calculation (or other statistics) would be? I mean what would you do to get, as olivier said a "generic resampling method to estimate variance or confidence intervals"? I'm under the impression that I need to define my own function for this as it is not exactly what I had in mind?
For genericbootstrap confidence intervals you can use scikits.bootstrap (a small separate project). I would personally be in favor of having such tools in scipy.stats by default though. > Also it seems that shuffle and split (I call it permutation) is also an iterator for cross-validation (same confusion about bootstraping)? Yes but contrary to our deprecated Bootstrap class, the shuffle & split strategy is a standard way to prepare folds for cross validation. You can see it as a generalization of iterated randomized k fold cross validation where you decouple test fold size from the number of folds / iterations.
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