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Criteo benchmark #52
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Some questions that arose from this work:
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For (2), typical approaches include over/under sampling (e.g. https://github.com/scikit-learn-contrib/imbalanced-learn), and using sample weights (which most sklearn estimators support). (3): L1 reg won't work with L-BFGS. As mentioned in the discussion for #40 there is the "trick" to use L1 with L-BFGS, or one must use OWL-QN (such as https://pypi.python.org/pypi/PyLBFGS/0.1.3). You should be able to use (4): By "sparsification" do you mean one-hot-encoding or feature hashing type approaches? As it seems you've used feature hashing here? (which is what I've been using for Criteo data too). Sklearn's relevant transformers are OneHotEncoder and FeatureHasher. |
I tried out dask-glm on a subset of the criteo data here:
https://gist.github.com/mrocklin/1a1c0b011e187a750a050eb330ac36b2
This used the following:
I suspect that there is still a fair amount to do here to optimize performance and quality of the model
cc @moody-marlin @TomAugspurger @MLnick
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