Forest-type Regression with General Losses and Robust Forest


Alexander Hanbo Li, Andrew Martin ;
Proceedings of the 34th International Conference on Machine Learning, PMLR 70:2091-2100, 2017.


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. We then use robust loss functions to develop more robust forest-type regression algorithms. In the experiments, we show by simulation and real data that our robust forests are indeed much more insensitive to outliers, and choosing the right number of nearest neighbors can quickly improve the generalization performance of random forest.

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