Here is a possibly useful comment of larsmans on stackoverflow about
exactly this procedure
http://stackoverflow.com/questions/26604175/how-to-predict-a-continuous-dependent-variable-that-expresses-target-class-proba/26614131#comment41846816_26614131
On Mon, Oct 10, 2016 at 4:04 PM, Sean Violant
sorry yes there was a misunderstanding:
I meant for each feature configuration you should pass in two rows (one for
the positive cases and one for the negative)
and the sample weight being the corresponding count for that configuration
and class
and I am saying that the total count is important
On 10 October 2016 at 12:22, Sean Violante wrote:
> no ( but please check !)
>
> sample weights should be the counts for the respective label (0/1)
>
> [ I am actually puzzled about the glm help file - proportions loses how
> often an input data 'row' was present relative to the other - though you
no ( but please check !)
sample weights should be the counts for the respective label (0/1)
[ I am actually puzzled about the glm help file - proportions loses how
often an input data 'row' was present relative to the other - though you
could do this by repeating the row 'n' times]
On Mon, Oct 1
How do I use sample_weight for my use case?
In my case is "y" an array of 0s and 1s and sample_weight then an
array real numbers between 0 and 1 where I should make sure to set
sample_weight[i]= 0 when y[i] = 0?
Raphael
On 10 October 2016 at 12:08, Sean Violante wrote:
> should be the sample we
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 wrote:
> I just noticed this about the glm package in R.
> http://stats.stackexchange.com/a/26779/53128
>
> "
> Th
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 i
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
