quantile-forest: A Python Package for Quantile Regression Forests
Quantile regression forests (QRF) is a non-parametric, tree-based ensemble method for estimating conditional quantiles (Meinshausen, 2006).It is a generalization of the random forests algorithm, a versatile ensemble learning algorithm, originally proposed in (Breiman, 2001), that has proven extremely popular and useful as a general-purpose machine learning method (Athey et al., 2019;Biau & Scornet, 2016;Hengl et al., 2018;Wager & Athey, 2018).Instead of outputting the weighted mean value of training labels like random forests regressors, QRF employs the weighted empirical distribution of training labels to obtain the predictive distribution.This feature enables QRF to output probabilistic predictions for regression problems, which are widely useful for constructing estimates of uncertainty (Petropoulos et al., 2022).