I'm doing a bunch of ML on a very different data set -- looking at what people eat (survey data). What's interesting to me is that if you do principal component

I'm doing a bunch of ML on a very different data set -- looking at what people eat (survey data). What's interesting to me is that if you do principal component analysis, for instance, there are some differences between the boys & girls in the sample, but they're not very distinct. If you do clustering or random forests on the dietary intakes of the whole cohort, you get mushy and unclear signals. If you split the survey respondents and bin by age and gender, and run different models for each, suddenly signals jump out of clustering incredibly clearly! What's weirdest is that you get some of the same dietary clusters for the different demographic groups -- but those clusters were not evident when you did clustering across the cohort. It's surprising to me that Amazon didn't (apparently) try different models for different populations. Sure, it might open you up to criticism, but there are some good data-driven reasons to do so. Women's colleges won't show up with regularity on men's resumes, for instance. Similarly, there are fraternities and sororities around engineering and STEM that may provide different signals, but won't appear equally distributed on men's & women's resumes. Language use on resumes does differ by gender, and using "Captured value of $100 million by..." rather than "Created value of $100 million by..." may describe the same project. (I gotta say, using verbs at all seems silly, since it really is about how well you market, rather than what you did.) So, curious about the model. Different models for different subsets of the training data can lead to big wins.