RFpredInterval: An R Package for Prediction Intervals with Random Forests and Boosted Forests

Autor:	Alakus, Cansu, Larocque, Denis, Labbe, Aurelie
Rok vydání:	2021
Předmět:	Statistics - Machine Learning Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	Like many predictive models, random forests provide point predictions for new observations. Besides the point prediction, it is important to quantify the uncertainty in the prediction. Prediction intervals provide information about the reliability of the point predictions. We have developed a comprehensive R package, RFpredInterval, that integrates 16 methods to build prediction intervals with random forests and boosted forests. The set of methods implemented in the package includes a new method to build prediction intervals with boosted forests (PIBF) and 15 method variations to produce prediction intervals with random forests, as proposed by Roy and Larocque (2020). We perform an extensive simulation study and apply real data analyses to compare the performance of the proposed method to ten existing methods for building prediction intervals with random forests. The results show that the proposed method is very competitive and, globally, outperforms competing methods. Comment: 36 pages, 14 figures, 5 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.08217 Zobrazit plný text záznamu View this record from Arxiv