Locally Optimized Random Forests
This addresses the challenge of distribution shift in supervised learning for applications such as forecasting power outages during extreme hurricanes, though it is incremental as it adapts existing random forests with a known sampling scheme.
The paper tackles the problem of making predictions when test data comes from a different distribution than training data, specifically under covariate shift, by proposing Locally Optimized Random Forests that use importance sampling weights based on estimated likelihood ratios, resulting in improved adaptation to extreme events like hurricanes.
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. We consider the case of having many labeled observations from one distribution, $P_1$, and making predictions at unlabeled points that come from $P_2$. We combine the high predictive accuracy of random forests (Breiman, 2001) 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 correspond to a non-parametric 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 (Shimodaira, 2000.) This methodology is motivated by the problem of forecasting power outages during hurricanes. The extreme nature of the most devastating hurricanes means that typical validation set ups will overly favor less extreme storms. Our method provides a data-driven means of adapting a machine learning method to deal with extreme events.