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.
Hanbo Li (UC San Diego)
Andy Martin (Zillow)
Related Events (a corresponding poster, oral, or spotlight)
2017 Talk: Forest-type Regression with General Losses and Robust Forest »
Tue Aug 8th 04:24 -- 04:42 AM Room C4.5