Random Forest based Qantile Oriented Sensitivity Analysis indices estimation
Kevin Elie-Dit-Cosaque, V\'eronique Maume-Deschamps (ICJ, PSPM)
TL;DR
This paper introduces a random forest-based method for estimating Quantile Oriented Sensitivity Analysis indices, incorporating cross-validation for leaf size optimization, validated on simulated and real datasets.
Contribution
It presents a novel estimation procedure for QOSA indices using random forests with an integrated cross-validation step for leaf size tuning.
Findings
Effective estimation demonstrated on simulated data.
Validated approach on real dataset.
Improved efficiency through leaf size optimization.
Abstract
We propose a random forest based estimation procedure for Quantile Oriented Sensitivity Analysis-QOSA. In order to be efficient, a cross validation step on the leaf size of trees is required. Our full estimation procedure is tested on both simulated data and a real dataset.
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Taxonomy
TopicsProbabilistic and Robust Engineering Design · Statistical Methods and Inference · Gaussian Processes and Bayesian Inference