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[D] Why does hierarchical Bayesian regression work well on imbalanced data?

I have a dataset of electrical outages and it is extremely imbalanced, <2% of all of the data are positive classes. I am using weather station data to try to predict the probability of an outage occurring near the weather stations.

When I try any other model I have to rebalance the data to get any good results. However I have recently tried hierarchical Bayesian logistic regression and it performs just fine without resampling. In my methodology every individual weather station has a unique intercept and coefficients, but they are each drawn from a parent distribution.

What I would like to discuss is why does the hierarchical approach perform so much better on the imbalanced dataset?

submitted by /u/paulie007
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.