should be the sample weight function in fit http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
On Mon, Oct 10, 2016 at 1:03 PM, Raphael C <drr...@gmail.com> wrote: > I just noticed this about the glm package in R. > http://stats.stackexchange.com/a/26779/53128 > > " > The glm function in R allows 3 ways to specify the formula for a > logistic regression model. > > The most common is that each row of the data frame represents a single > observation and the response variable is either 0 or 1 (or a factor > with 2 levels, or other varibale with only 2 unique values). > > Another option is to use a 2 column matrix as the response variable > with the first column being the counts of 'successes' and the second > column being the counts of 'failures'. > > You can also specify the response as a proportion between 0 and 1, > then specify another column as the 'weight' that gives the total > number that the proportion is from (so a response of 0.3 and a weight > of 10 is the same as 3 'successes' and 7 'failures')." > > Either of the last two options would do for me. Does scikit-learn > support either of these last two options? > > Raphael > > On 10 October 2016 at 11:55, Raphael C <drr...@gmail.com> wrote: > > I am trying to perform regression where my dependent variable is > > constrained to be between 0 and 1. This constraint comes from the fact > > that it represents a count proportion. That is counts in some category > > divided by a total count. > > > > In the literature it seems that one common way to tackle this is to > > use logistic regression. However, it appears that in scikit learn > > logistic regression is only available as a classifier > > (http://scikit-learn.org/stable/modules/generated/sklearn.linear_model. > LogisticRegression.html > > ) . Is that right? > > > > Is there another way to perform regression using scikit learn where > > the dependent variable is a count proportion? > > > > Thanks for any help. > > > > Raphael > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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