Extensions to quantile regression forests for very high-dimensional data

This paper describes new extensions to the state-of-the-art regression random forests Quantile Regression Forests (QRF) for applications to high-dimensional data with thousands of features. We propose a new subspace sampling method that randomly samples a subset of features from two separate feature sets, one containing important features and the other one containing less important features. The two feature sets partition the input data based on the importance measures of features. The partition is generated by using feature permutation to produce raw importance feature scores first and then applying p-value assessment to separate important features from the less important ones. The new subspace sampling method enables to generate trees from bagged sample data with smaller regression errors. For point regression, we choose the prediction value of Y from the range between two quantiles Q_0.05 and Q_0.95 instead of the conditional mean used in regression random forests. Our experiment results have shown that random forests with these extensions outperformed regression random forests and quantile regression forests in reduction of root mean square residuals.