I think he wrote the comment in bad faith. The non-invertability is part of the problem, and he completely doesn't understand that. So at best he just wants to argue about race and has no interest in the technical material. Anyway, even if you had "ratio of blacks to whites", that still wouldn't be a very good piece of data, because it says nothing about the geographical distribution or diversity of social interactions which is what people mean when they say a neighborhood is "integrated".

I don't see why non-invertibility matters. Lots of useful features are non-invertible.

Edit: and if you are dealing with real data sets or producing real datasets for analysis you will often have only approximations to the thing you want to measure. Determining whether your proxy variable is worth including or how to interpret your results in light of it are necessary skills to develop.

The feature is bad. The non-invertibility means that you cannot get back the original data that was used to generate the feature, and try to salvage it.

Sure, that makes it less useful. But why is that so bad that the entire dataset should be discarded and not used, even for uses that don't care about that particular part of the original data?

If you want the dataset, scikit even tells you how to get it. If you just want an example dataset, there are better ones. I mean, this seems somewhat like the Lena debacle: why insist on this particular dataset?

> Please respond to the strongest plausible interpretation of what someone says, not a weaker one that's easier to criticize. Assume good faith.

https://news.ycombinator.com/newsguidelines.html

Sadly, assumptions of good faith are easily exploited by bad actors (the classic term for this is "just asking questions") but I suppose you're right, I should not have assumed malice.

Better one one mud slinger than two, for the sake of the community. Guilty of it myself too many times. Cheers.

> The non-invertability is part of the problem, and he completely doesn't understand that.

I get that invertibility means that you can't fully recover the original racial percentage, e.g., that a 48/52 split and a 78/22 split will both look exactly the same, since (.48-.63)^2 and (.78-.63)^2 are equal. I don't see why that totally taints the entire dataset.

> I think he wrote the comment in bad faith. The non-invertability is part of the problem, and he completely doesn't understand that.

If he doesn't understand it, then it's not in bad faith. Right?