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.

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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.

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