From 666dd81a3d896d7285d57630ad62d0e9bd1b9fa7 Mon Sep 17 00:00:00 2001 From: Reid Johnson Date: Wed, 28 Aug 2024 23:29:29 -0700 Subject: [PATCH] Update docs --- docs/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/index.rst b/docs/index.rst index 5170d93..069839b 100755 --- a/docs/index.rst +++ b/docs/index.rst @@ -11,7 +11,7 @@ quantile-forest **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 :cite:t:`2006:meinshausen`. 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. + 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 :cite:t:`2006:meinshausen`. 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 forest regressors. .. grid:: 1 1 2 2 :padding: 0 2 3 5