quantile-forest#
Version: 1.3.5
quantile-forest is an implementation of scikit-learn compatible quantile regression forests.
Quantile regression forests (QRF) are a non-parametric, tree-based ensemble method for estimating conditional quantiles, with application to high-dimensional data and uncertainty estimation. The estimators in this package are performant, Cython-optimized QRF implementations that extend the forest estimators available in scikit-learn to estimate conditional quantiles, as described by Meinshausen [Mei06]. The estimators can estimate arbitrary quantiles at prediction time without retraining and provide methods for out-of-bag estimation, calculating quantile ranks, and computing proximity counts. They are compatible with and can serve as drop-in replacements for the scikit-learn variants.
A guide that provides installation requirements and instructions, as well as procedures for developers.
Information on the key concepts behind quantile forests and how they apply to this package.
Examples that demonstrate the broad applications and introductory concepts of quantile forests.
Information on all of the package methods and classes, for when you want just the details.