For that reason, I'd be happier if they sorted by KL Divergence instead of just log-odds. That'd give a much better tradeoff between commonality and predictive power.
You have a valid point but I'm guessing they avoided kl-divergence because it's 1) harder to explain 2) it's not symmetric, e.g. kl(asian, white) != kl(white, asian) 3) it needs a smoothing function for comparing distributions where not every element is in both distributions.
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For that reason, I'd be happier if they sorted by KL Divergence instead of just log-odds. That'd give a much better tradeoff between commonality and predictive power.
You have a valid point but I'm guessing they avoided kl-divergence because it's 1) harder to explain 2) it's not symmetric, e.g. kl(asian, white) != kl(white, asian) 3) it needs a smoothing function for comparing distributions where not every element is in both distributions.