Forest Guided Smoothing

Isabella Verdinelli; Larry Wasserman

Forest Guided Smoothing

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arxiv 2103.05092 v1 pith:KNH4ME7O submitted 2021-03-08 stat.ML cs.LGstat.ME

Isabella Verdinelli , Larry Wasserman This is my paper · ORCID

classification stat.MLcs.LGstat.ME

keywords forestoriginalsmootheradaptiveassessingbandwidthbiasconfidence

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We use the output of a random forest to define a family of local smoothers with spatially adaptive bandwidth matrices. The smoother inherits the flexibility of the original forest but, since it is a simple, linear smoother, it is very interpretable and it can be used for tasks that would be intractable for the original forest. This includes bias correction, confidence intervals, assessing variable importance and methods for exploring the structure of the forest. We illustrate the method on some synthetic examples and on data related to Covid-19.

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