Performance Study of Prediction Intervals with Random Forest for Poverty Data Analysis - Valentika, Nina; Notodiputro, Khairil Anwar; Sartono, Bagus (Jurnal Aplikasi Statistika & Komputasi Statistik, 2024)
Introduction/Main Objectives: Determine the prediction interval with for analyzing poverty data at the Regency/City level in Indonesia. Background Problems: Poverty will be a topic in various discussion and debates in the future. Novelty: This study’s methods for constructed prediction intervals are LM, Quant, SPI, HDR, and CHDR. This method can improve the prediction interval performance with Random Forests. Research Methods: The method for building forests and obtaining BOP in this study is CART with the LS splitting rule. Finding/Results: The results of this study are that the best method for one replication is HDR with 500 trees. The best method for 100 repetitions is LM. Based on hypothesis testing, there is sufficient evidence to say no difference between the LM, SPI, Quant, HDR, and CHDR methods for 100 replications at a 5% significance level.