[joeraii]

yeah pretty strong l1--most features were 0. we binarized rank on I_{rank<=20}. it turns out there are tons of articles beyond the first page that stay low forever. check out the interactive viz vad made: http://hn.metamx.com (warning 2.6MB compressed js ahead)

[brendano]

Another question, how are standard errors calculated? I assume they're not from the bootstrapping since the p-values clearly aren't from the standard errors ( +/- 1.96*se is crossing coef=0 for several cases but with small p-values). The other way I would think to get p-values would be the percentage of bootstrap replicates that have (coef==0). But for only 20 replicates you're stuck with p=0 or p=0.05.

I'm genuinely curious how to do coef significance testing for L1-regularized models. I once saw someone ask this at a Tibshirani talk and he said "oh we have no idea, we've resorted to the bootstrap before".

[joeraii]

to be honest we just recorded the coeff values for each replicate and did the bootstrap variance calculation.

% of replicates with (coef==0) is potentially much more clever, especially since that's the test we want to perform anyway. i'll run that over the data and see what changes.

[equark]

I think the question is these don't look like NormalCDF(coef/se) p-values given the coef and se you report. They tend to be too small.

From a frequentist perspective, counting zeroes don't make much sense because under the null of coef=0 there is still a chance you don't estimate coef=0, even after regularization.

[brendano]

    I think the question is these don't look like NormalCDF(coef/se) p-values given the coef and se you report.  They tend to be too small.

right that's my question

[joeraii]

interesting yeah some of them definitely don't look right. the output is from scipy's stats.ttest_1samp