Two-level quantile regression forests for bias correction in range prediction

Quantile regression forests (QRF), a tree-based ensemble method for estimation of conditional quantiles, has been proven to perform well in terms of prediction accuracy, especially for range prediction. However, the model may have bias and suffer from working with high dimensional data (thousands of features). In this paper, we propose a new bias correction method, called bcQRF that uses bias correction in QRF for range prediction. In bcQRF, a new feature weighting subspace sampling method is used to build the first level QRF model. The residual term of the first level QRF model is then used as the response feature to train the second level QRF model for bias correction. The two-level models are used to compute bias-corrected predictions. Extensive experiments on both synthetic and real world data sets have demonstrated that the bcQRF method significantly reduced prediction errors and outperformed most existing regression random forests. The new method performed especially well on high dimensional data.