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

06/15/2021 ∙ by Cansu Alakus, et al. ∙ 0

Like many predictive models, random forests provide a point prediction for a new observation. 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 methods implemented in the package are a new method to build prediction intervals with boosted forests (PIBF) and 15 different variants to produce prediction intervals with random forests 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 to build prediction intervals with random forests. The results show that the proposed method is very competitive and, globally, it outperforms the competing methods.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 2

page 3

page 4

Code Repositories

RFpredInterval

Prediction Intervals with Random Forests and Boosted Forests


view repo
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.