# Tabular Data Models for Predicting Art Auction Results
###### Abstract  
Predicting art auction results presents a unique challenge due to the complexity and variability of factors influencing artwork prices. This study explores a range of machine learning architectures designed to forecast auction outcomes using tabular data, including historical auction records, artwork characteristics, artist profiles, and market indicators. We evaluate traditional models such as LinearModel, K-Nearest Neighbors, DecisionTree, RandomForest, XGBoost, CatBoost, LightGBM, MLP, VIME, ModelTree, DeepGBM, DeepFM, and SAINT. By comparing the performance of these models on a dataset comprising extensive auction results, we provide insights into their relative effectiveness across different scenarios. Additionally, we address the interpretability of models, which is crucial for understanding the influence of various features on predictions. The results suggest that while some models perform better than others, no single approach offers consistently high accuracy across all cases. This study provides guidance for auction houses, art investors, and market analysts in refining predictive approaches, identifying key challenges, and understanding where further improvements are needed for more accurate data-driven decisions in the art market. tabular data predictions; machine learning architectures; neural networks; decision trees +
Footnote †: journal: Applied sciences  
**Citation:** [PERSON]; [PERSON] [PERSON]. **Tabulular Data Models for Predicting Art Auction Results. ** _Appl. Sci._**2024**, 14, 11006. [[https://doi.org/10.3390/app142311006](https://doi.org/10.3390/app142311006)]([https://doi.org/10.3390/app142311006](https://doi.org/10.3390/app142311006))
Traditional statistical methods, such as Linear Models, K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) have long been used to model auction outcomes, but they often fall short in capturing non-linear relationships and interactions among variables [4]. Recent advancements in machine learning have introduced more sophisticated approaches, such as Decision Trees, Random Forests, Gradient Boosting Machines (including XGBoost [5], CatBoost [6]), and Neural Networks (such as Multi-Layer Perceptron (MLP) and DeepGBM [7]), which offer improved predictive accuracy and the ability to handle large, complex datasets. Additionally, modern approaches like VIME [8], RLN [9], ModelTree [10], DeepFM [11], and SAINT [12] have been explored for their potential in tackling the nuanced challenges of auction data prediction. However, the art market's inherent unpredictability and the need for model interpretability present ongoing challenges. Several publications have explored the application of machine learning in the art market, highlighting both the potential and limitations of current methodologies [13; 14; 15]. Some studies have focused on the predictive power of specific features [16], while others have investigated the use of ensemble models to improve accuracy [17]. Despite these advances, there is still debate over the most effective modelling strategies. This study aims to provide a comprehensive evaluation of various machine learning architectures for predicting art auction results using tabular data. By systematically comparing traditional models with more advanced techniques, we seek to identify the strengths and weaknesses of each approach. Our analysis includes an exploration of model interpretability, a crucial aspect for stakeholders who need to understand the factors driving predictions. The principal conclusion of this work is that while advanced models generally offer better predictive performance, their complexity necessitates careful consideration of interpretability, especially in a market as nuanced as art. In summary, this research contributes to the ongoing discourse on the application of machine learning in the art market by offering insights into the most effective prediction architectures and by providing a collected dataset of real-world European online auction results.