On Mon, Oct 5, 2015 at 6:15 PM, Sturla Molden <sturla.mol...@gmail.com> wrote:
> On 04/10/15 05:07, George Bezerra wrote: > > > I am trying to follow this paper: > > > http://research.microsoft.com/en-us/um/people/mattri/papers/www2007/predictingclicks.pdf > > (check out section 6.2). They use logistic regression as a regression > > model to predict the click through rate (which is continuous). > > I am not sure what to think about this, though I don't have time to look > at it in detail. But modelling rates is usually a case for Poisson > regression rather than logistic regression. Rate and probability is not > the same. > rate in the sense of proportion is between zero and 1. y percent of all users that are at this stage click or buy. Any continuous response on a known interval can be mapped to [0, 1] and be modeled with Logistic regression (or GLM Binomial in general). Poisson is for non-negative numbers (real or float) without (known) upper bound. One distribution that is defined for continuous proportions/rates/probabilities would be Beta, and BetaRegression would be the two parameter regression model. Josef > > > > A linear regression model will violate the assumption that probabilities > > vary between 0 and 1 (it will give me values outside this range in some > > cases). I would think it is in principle possible to solve the logistic > > regression for a continuous value, although scikit doesn't support it. > > The word you are looking for is 'generalized linear model'. > > > 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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