Enhancing Registration Offices’ Communication Through Interpretable Machine-Learning Techniques

Enhancing Registration Offices’ Communication Through Interpretable Machine-Learning Techniques

This study presents a protocol for applying Interpretable Machine Learning (IML) to enhance communication within Variety Registration Offices (VROs). Rather than focusing on a model comparison, we illustrate how two IML-compatible models—Random Forests and AMBARTI—can support a clearer interpretatio...

Full description

Saved in:
Bibliographic Details
Main Authors: Danilo Augusto Sarti, Tommaso Bardelli, Pier Giacomo Bianchi, Anna Pia Maria Giulini
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Agronomy
Subjects:
wheat genotypes
protein content
yields
AMBARTI models
Random Forest models
Online Access:https://www.mdpi.com/2073-4395/15/7/1603
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://www.mdpi.com/2073-4395/15/7/1603

Similar Items