A random forest that reweights training data by an estimated likelihood ratio between training and test covariate distributions improves prediction under covariate shift, with modest and uneven real-data gains.
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Locally Optimized Random Forests
A random forest that reweights training data by an estimated likelihood ratio between training and test covariate distributions improves prediction under covariate shift, with modest and uneven real-data gains.