On Mon, Oct 5, 2015 at 10:05 PM, Sturla Molden <sturla.mol...@gmail.com> wrote:
> On 06/10/15 00:35, josef.p...@gmail.com wrote: > > > rate in the sense of proportion is between zero and 1. > > Rate usually refers to "events per unit of time or exposure", so we can > either count events in intervals or record time-stamps as our dependent > variable. If the stochastic counting process is memoryless we have a > Poisson process. > > Poisson regression can often be used to model this type of data. > > Rate in the sense of proportion between 0 and 1 is not really a rate. > But sure, there are many ways to model such data, including assuming a > beta distribution for the proportion. > I have seen lots of variation on the various terms across fields. In the current context click-through-rate is a conditional probability, AFAICS from a quick browsing of the article. Since the rate/probability is pretty low 2-5%, I guess the constraint < 1 won't be relevant and any regression method for non-negative valued response should work, including Poisson. It might be more relevant what the local nonlinearity should be (which link function in terms of GLM). For classification it sounds like a very unbalanced case. Josef > > > Sturla > > > > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general >
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