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family parameter #3

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idiazst opened this issue Aug 31, 2017 · 7 comments
Open

family parameter #3

idiazst opened this issue Aug 31, 2017 · 7 comments
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@idiazst
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idiazst commented Aug 31, 2017

Is there a reason not to have a family ('gaussian' or 'binomial') parameter and pass it to glmnet?

@nhejazi
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nhejazi commented Aug 31, 2017

Based on my (likely limited) understanding, there's no theoretical reason that requires us to hardcode the default of family = "gaussian" as we do right now. Simply a matter of adding ... to the main function. @benkeser and @osofr, please feel free to correct me if I'm missing something.

@benkeser
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benkeser commented Sep 7, 2017

I hesitated to do this for a theoretical reason, though I think in practice, it is probably fine to include a family argument.

My hesitation came from the following line of thinking: we know that any function with bounded variation norm can be represented using an indicator basis function parameterization that we use. However, it's not immediately clear to me that this remains true on the expit scale, i.e., not sure that it's true that we can write equation (1) from our paper on the expit scale. I honestly am not sure whether this is true or even relevant. Again, my hunch is that it'd be fine in practice. However, for the time being I'm more comfortable truncating predictions > 1 or < 0.

@jlstiles
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jlstiles commented Sep 7, 2017

Maybe I am missing something but really we are only representing a probability function as opposed to a function that is outside 0-1 possibly. Then you just fit Y = expit(linear function) except with log-lik-loss and constraint on coeffs. Perhaps I'm being thick but don't see what the issue is. The outcome plays no role in the basis functions.

@jlstiles
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jlstiles commented Sep 7, 2017

Oh nevermind, yes I have to think about that, too

@jlstiles
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jlstiles commented Sep 7, 2017

log odds of the true prob function is fit at the required rate so it is just a question of the transformed one which must as well. The loss is valid of course so I think we are good

@benkeser
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benkeser commented Sep 7, 2017

Makes sense to me.

@idiazst
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idiazst commented Sep 8, 2017 via email

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