Online Program

Locally Optimized Random Forests: A Solution to Forecasting Severe Hurricane Power Outages (308388)

Presentation

*Tim Coleman, University of Pittsburgh, Department of Statistics 
Mary Frances Dorn, Los Alamos National Laboratory, CCS-6 
Kim Kaufeld, Los Alamos National Laboratory, CCS-6 
Lucas Mentch, University of Pittsburgh 

Keywords: random forest, covariate shift, hurricane

Standard supervised learning procedures are validated against a test set that is assumed to have come from the same distribution as the training data. However, in many problems, the test data may have come from a different distribution. To this end, we combine the high predictive accuracy of random forests with an importance sampling scheme, where the splits and predictions of the base-trees are done in a weighted manner, which we call Locally Optimized Random Forests. These weights are an estimate of the likelihood ratio between the training and test distributions. To estimate these ratios with an unlabeled test set, we make the covariate shift assumption, where the differences in distribution are only a function of the training distributions. This methodology is motivated by the problem of forecasting power outages during hurricanes.