Explainable Machine Learning Method for Aesthetic Prediction of Doors and Home Designs

Aug 5, 2026

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Interior home design increasingly relies on machine learning, yet aesthetics remain highly subjective and vary across individuals and cultures. The study presents an applied explainable machine learning approach to improve manufactured custom doors within an aesthetically appropriate home design context. Millions of door model combinations make universal design prediction infeasible, so experts generate labeled door–home design datasets. A random forest classifier learns supervised mappings and outputs suitable home design predictions, achieving 86.8% accuracy and supporting interpretability via feature importance, trees, and error analysis.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages14

Reading Time35 min

AccessFree to download

Introduction

Background and motivation

Related work and aesthetic evaluation

Machine learning for home and furniture aesthetics

Method and data generation

Dataset labeling for door–design combinations

Random forest training and prediction workflow

Explainability and evaluation

Accuracy results

Interpretable outputs: feature importance, decision tree, confusion matrix

information Article Explainable Machine Learning Method for Aesthetic Prediction of Doors and Home Designs Jean-Sébastien Dessureault 1,2, *, Félix Clément 1, Seydou Ba 1, François Meunier 1 and Daniel Massicotte 2 Citation: Dessureault, J.-S.; Clément, F.; Ba, S.; Meunier, F.; Massicotte, D. Explainable Machine Learning Method for Aesthetic Prediction of Doors and Home Designs. Information 2024, 15, 203. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/info15040203](10.3390/info15040203) Academic Editors: Gabriele Gianini and Pierre-Edouard Portier Received: 21 February 2024 Revised: 24 March 2024 Accepted: 29 March 2024 Published: 5 April 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) . 1 Departement of Mathematics and Computer Science, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada; [felix.clement@uqtr.ca](felix.clement@uqtr.ca) (F.C.); [seydou.ba@uqtr.ca](seydou.ba@uqtr.ca) (S.B.); [francois.meunier@uqtr.ca](francois.meunier@uqtr.ca) (F.M.) 2 Departement of Electrical and Computer Engineering, Université du Québec à Trois-Rivières, Trois-Rivières, QC G8Z 4M3, Canada; [daniel.massicotte@uqtr.ca](daniel.massicotte@uqtr.ca) * [Correspondence: jean-sebastien.dessureault@uqtr.ca](Correspondence: jean-sebastien.dessureault@uqtr.ca); Tel.: +1-819-376-5011 (ext. 3827) Abstract: The field of interior home design has witnessed a growing utilization of machine learning. However, the subjective nature of aesthetics poses a significant challenge due to its variability among individuals and cultures. This paper proposes an applied machine learning method to enhance manufactured custom doors in a proper and aesthetic home design environment. Since there are millions of possible custom door models based on door types, wood species, dyeing, paint, and glass types, it is impossible to foresee a home design model fitting every custom door. To generate the classification data, a home design expert has to label thousands of door/home design combinations with the different colors and shades utilized in home designs. These data train a random forest classifier in a supervised learning context. The classifier predicts a home design according to a particular custom door. This method is applied in the following context: A web page displays a choice of doors to a customer. The customer selects the desired door properties, which are sent to a server that returns an aesthetic home design model for this door. This door configuration generatesa series of images through the Unity 3D engine module, which are returned to the web client. The customer finally visualizes their door in an aesthetic home design context. The results show the random forest classifier’s good performance, with an accuracy level of 86.8%, in predicting suitable home design, marking the way for future developments requiring subjective evaluations. The results are also explained using a feature importance graphic, a decision tree, a confusion matrix, and text. Keywords: applied machine learning; aesthetic prediction; explainability; random forest algorithm 1. Introduction Machine learning has been increasingly applied to the field of aesthetics, particularly in the context of furniture and home design. One of the critical challenges in this domain is the subjective nature of aesthetics, which can vary significantly across individuals and cultures. However, recent studies have shown that machine learning algorithms can learn to recognize patterns and features commonly associated with aesthetic preferences and use this knowledge to predict new designs. For example, the authors of [1] implemented an in

FAQs

Why is aesthetic prediction for home designs challenging for machine learning?

Aesthetics are subjective and vary significantly across individuals and cultures, making consistent labeling and prediction difficult.

How are training data for the door-to-home-design classifier generated?

A home design expert labels thousands of door/home design combinations, including color and shade variations used in home designs.

What model is used to predict an aesthetic home design for a selected custom door?

A random forest classifier is trained in a supervised learning setting, then predicts the home design corresponding to a particular custom door.

Aug 5, 2026

Upload by Maeve