Quantregforest

quantregForest: Quantile Regression Forests. Quantile Regression Forests is a tree-based ensemble method for estimation of conditional quantiles. It is particularly well suited for high-dimensional data. Predictor variables of mixed classes can be handled. The package is dependent on the package randomForests, written by Andy Liaw.

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References in zbMATH (referenced in 6 articles )

Showing results 1 to 6 of 6.
Sorted by year (citations)

  1. Cansu Alakus, Denis Larocque, Aurelie Labbe: RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests (2021) arXiv
  2. Sage, Andrew J.; Genschel, Ulrike; Nettleton, Dan: Tree aggregation for random forest class probability estimation (2020)
  3. Au, Timothy C.: Random forests, decision trees, and categorical predictors: the “absent levels” problem (2018)
  4. Wang, Qiang; Nguyen, Thanh-Tung; Huang, Joshua Z.; Nguyen, Thuy Thi: An efficient random forests algorithm for high dimensional data classification (2018)
  5. Nguyen, Thanh-Tung; Huang, Joshua Z.; Nguyen, Thuy Thi: Two-level quantile regression forests for bias correction in range prediction (2015)
  6. Roy, Marie-Hélène; Larocque, Denis: Robustness of random forests for regression (2012)