Prediction Intervals via Support Vector-quantile Regression Random Forest Hybrid
Vadlamani Ravi, Vadali Tejasviram, Anurag Sharma, Rashmi Ranjan Khansama · 2017
This paper presents a new method of determining prediction intervals via the hybrid of support vector machine and quantile regression random forest introduced elsewhere. Its effectiveness is tested on 5 benchmark regression problems. Fromthe experiments, we infer that the difference in performance of the prediction intervals from the proposed method and those from quantile regression and quantile regression random forest is statistically significant as shown by the Wilcoxon test at 5% level of significance. This is an important achievement of the paper.