AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Models

TL;DR

AHMoSe is a visual support system that helps agricultural experts understand, diagnose, and compare regression models by integrating domain knowledge, especially useful when datasets are limited or models have similar performance.

Contribution

The paper introduces AHMoSe, a novel visual support system that incorporates domain knowledge into model explanations for improved model selection in agriculture.

Findings

AHMoSe enables better model diagnosis and comparison.

Domain knowledge integration improves model selection accuracy.

Feedback indicates the system is useful for both ML and domain experts.

Abstract

Decision support systems have become increasingly popular in the domain of agriculture. With the development of automated machine learning, agricultural experts are now able to train, evaluate and make predictions using cutting edge machine learning (ML) models without the need for much ML knowledge. Although this automated approach has led to successful results in many scenarios, in certain cases (e.g., when few labeled datasets are available) choosing among different models with similar performance metrics is a difficult task. Furthermore, these systems do not commonly allow users to incorporate their domain knowledge that could facilitate the task of model selection, and to gain insight into the prediction system for eventual decision making. To address these issues, in this paper we present AHMoSe, a visual support system that allows domain experts to better understand, diagnose and compare different regression models, primarily by enriching model-agnostic explanations with domain knowledge. To validate AHMoSe, we describe a use case scenario in the viticulture domain, grape quality prediction, where the system enables users to diagnose and select prediction models that perform better. We also discuss feedback concerning the design of the tool from both ML and viticulture experts.