4 ms·
Perhaps the same reason heat maps are often really the underlining population map https://xkcd.com/1138/ https://xkcd.com/1138/
by dataviz1000 2y ago
Perhaps the same reason heat maps are often really the underlining population map
https://xkcd.com/1138/ https://xkcd.com/1138/
- wodenokoto 2y agoThat’s why in NLP we use term frequency over inverse document frequency. It gives you a measure of common uncommon things are. Wonder how you’d implement that in a heat map. Just call each pixel a document and see where it takes you?
- supriyo-biswas 2y agoWinsorize the data points to remove outliers and then divide it by the population count for the case of the heatmap?
- bravura 2y agoPeople have been critiquing the collaborative filtering aspect of this work vs content analysis ("[why use stars instead of code similarity]") but there's something elegant about the simplicity of using less priors here. A tf*idf matrix could be applied to the star-feature matrix too. Document = github repo. Term = name of user who starred it. THUS, users who overstar are simply less important for computing similarities. This would mitigate the phenomenon of massively popular github repos being clustered together because of folks who blithely star the most well known stuff.