Using partial pooling when preparing data for machine learning applications

April 18, 2018
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(This article was originally published at Statistical Modeling, Causal Inference, and Social Science, and syndicated at StatsBlogs.)

Geoffrey Simmons writes:

I reached out to John Mount/Nina Zumel over at Win Vector with a suggestion for their vtreat package, which automates many common challenges in preparing data for machine learning applications.
The default behavior for impact coding high-cardinality variables had been a naive bayes approach, which I found to be problematic due its multi-modal output (assigning probabilities close to 0 and 1 for low sample size levels). This seemed like a natural fit for partial pooling, so I pointed them to your work/book and demonstrated it’s usefulness from my experience/applications. It’s now the basis of a custom-coding enhancement to their package.
You can find their write up here.
Cool.  I hope their next step will be to implement in Stan.
It’s also interesting to think of Bayesian or multilevel modeling being used as a preprocessing tool for machine learning, which is sort of the flipped-around version of an idea we posted the other day, on using black-box machine learning predictions as inputs to a Bayesian analysis.  I like these ideas of combining different methods and getting the best of both worlds.

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