@KirkegaardEmil https://quid.com/feed/how-quid-uses-deep-learning-with-small-data … example of the sort of approach I mean fpr handling your surnames by regression on n-grams.
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It's first name data. :) Data here with calculated S scores. https://osf.io/xf8py/ But yes, I should give it a go. Maybe now.
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What sort of R^2 do you get with all the n-grams? also, could use 'p.adjust' to do non-Bonferroni multiple correction.
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Like in multiple regression? Currently have about 280 ngrams and ~1900 names, so could use OLS MR and get CV R2.
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The p_cor is the corrected p value. glmnet not suitable for categorical predictors. Need a good function for LASSO with GLM.
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