IMPROVING ACCURACY OF PREDICTION INTERVALS OF HOUSEHOLD INCOME USING QUANTILE REGRESSION FOREST AND SELECTION OF EXPLANATORY VARIABLES

IMPROVING ACCURACY OF PREDICTION INTERVALS OF HOUSEHOLD INCOME USING QUANTILE REGRESSION FOREST AND SELECTION OF EXPLANATORY VARIABLES

Quantile regression forest (QRF) is a non-parametric method for estimating the distribution function of response by using the random forest algorithm and constructing conditional quantile prediction intervals. However, if the explanatory factors (covariates) are highly correlated, the quantile regre...

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Bibliographic Details
Main Authors: Asrirawan Asrirawan, Khairil Anwar Notodiputro, Bagus Sartono
Format: Article
Language:English
Published: Universitas Pattimura 2023-12-01
Series:Barekeng
Subjects:
household income
quantile regression forest
random forest
prediction interval
Online Access:https://ojs3.unpatti.ac.id/index.php/barekeng/article/view/8974
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