Comparison of Trivariate Copula-Based Conditional Quantile Regression Versus Machine Learning Methods for Estimating Copper Recovery

Comparison of Trivariate Copula-Based Conditional Quantile Regression Versus Machine Learning Methods for Estimating Copper Recovery

In this study, an innovative methodology using trivariate copula-based conditional quantile regression (CBQR) is proposed for estimating copper recovery. This approach is compared with six supervised machine learning regression methods, namely, Decision Tree, Extra Tree, Support Vector Regression (l...

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Bibliographic Details
Main Authors: Heber Hernández, Martín Alberto Díaz-Viera, Elisabete Alberdi, Aitor Goti
Format: Article
Language:English
Published: MDPI AG 2025-02-01
Series:Mathematics
Subjects:
metallurgical copper recovery
copula
quantile regression
kernel smoothing
machine learning
Online Access:https://www.mdpi.com/2227-7390/13/4/576

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