This package implements the LOWESS-RF method described by Sage, Genschel, and Nettleton. Random forest prediction weights are iteratively adjusted so that outlying cases are downweighted, resulting in a more robust prediction. Outlying cases are identified using a residual analysis. This approach is motivated by Cleveland's (1979) locally weighted regression and scatterplot smoothing technique.
|Maintainer||Andrew Sage <[email protected]>|
|License||What license is it under?|
|Package repository||View on GitHub|
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