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awarded Popular Question
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awarded Notable Question
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comment Randomly distributed cases according to census data
Thanks. I am still trying to understand my data too - I will try out your recommendations and see how they work out.
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awarded Supporter
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awarded Student
Nov
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asked Randomly distributed cases according to census data
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awarded Nice Question
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awarded Curious
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comment Variablity in cv.glmnet results
I like how you've done it. I have the same loop but with one exception at the end : I look at how frequently different features pop up as opposed to the lowest MSE from all iterations. I pick an arbitrary cut point (i.e. show up 50/100 iterations) and use those features. Curious contrast the two approaches.
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comment Variablity in cv.glmnet results
The thing I am most concerned about is that the selection of n really does seem to matter sometimes. Should I trust results that can be so variable? Or should I chalk it up as sketchy even if I do run it multiple times?
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asked Variablity in cv.glmnet results
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asked Bootstrapped optimism with low prevalence
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comment Multivariate regression with weighted least squares in python?
In what way is your response a 2-D vector? The description sounds like your response is one variable and that all your data X is presented by 2-D.
Mar
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comment Convert an image file into a shape file
Thank you - I will give that a try and see what I can do.
Mar
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asked Convert an image file into a shape file
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awarded Scholar
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accepted How much RAM does data take up?
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comment How much RAM does data take up?
@abarnert If things get to large, I will need it to act like an in-RAM dict ideally. Thank you for the module recommendations.
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comment How much RAM does data take up?
Data sets I typically work with are something called fastq files: en.wikipedia.org/wiki/FASTQ_format I know there are optimized methods for such data as well. I typically store each read (with its ID, sequence and quality) as a class and make a dictionary of type read. Eventually, that will run out of memory when I analyze something large enough.
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