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Forest-type Regression with General Losses and Robust Forest

ICML 2017 • Alexander Hanbo Li • Andrew Martin

This paper introduces a new general framework for forest-type regression which allows the development of robust forest regressors by selecting from a large family of robust loss functions. In particular, when plugged in the squared error and quantile losses, it will recover the classical random forest and quantile random forest... (read more)

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Forest-type Regression with General Losses and Robust Forest

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