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Not entirely sure I got the point of this paper/library. Is it simply that it improves accuracy, or is the user supposed to be able to (after training) leverage
by seertaak 5y ago
Not entirely sure I got the point of this paper/library. Is it simply that it improves accuracy, or is the user supposed to be able to (after training) leverage the RW components of the model, say, in a later forward/backward induction?
- disgruntledphd2 5y agoI mean, from my perspective, this is super useful, as I'm a fan of both boosting and mixed effects models. Really interesting work.
- zwaps 5y agoSince it incorporates mixed effects models my guess is that it allows you to leverage structure in your data to improve the efficiency (in the statistical sense) of the estimation. That is, you may have a panel of groups. So you might assume observations have in-group and out-group variances (or some other clustering of errors) and furthermore, observations come in time dimensions. Then, obviously, making use of that information will improve your "fit" in the sense that the regression error will be lower when you estimate across the whole sample, since the dependencies are taken into account. Something like that.