When

Start: 04/16/2018 - 4:15pm

End : 04/16/2018 - 5:15pm

End : 04/16/2018 - 5:15pm

Category

Applied Math Seminar

Speaker

Jo Hardin (Pomona College)

Abstract

Although random forests are commonly used for regression, our understanding

of the prediction error associated with random forest predictions of individual re-

sponses is relatively limited. We introduce a novel measure of this error and evaluate

its properties, comparing it with the out-of-bag mean of squared residuals estimator

that, to our knowledge, is the only measure of random forest prediction error that

has been introduced in the literature thus far. We show that our proposed estimator

provides an individualized estimate of the error associated with a particular random

forest prediction, while the out-of-bag mean of squared residuals estimator provides

a more general estimate of the random forest's prediction error as a whole. Through

simulations on benchmark and simulated datasets, we also demonstrate that both

estimators of prediction error may form the bases for valid random forest predic-

tion intervals. Empirically, these prediction intervals performed as well as quantile

regression forest prediction intervals.

Where

Emmy Noether Rm
Millikan 1021
Pomona College

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