# Tabular Data Models for Predicting Art Auction Results
## 4 Discussion  
The results of this study reveal persistent challenges in applying machine learning models to the prediction of art auction results. While tree-based ensemble models, particularly XGBoost and Random Forest, managed to perform better than other approaches, their effectiveness was still limited when dealing with the complexities of tabular data. Although these models demonstrated some predictive power across the auction datasets, their performance fell short of consistently robust results. This outcome contrasts with prior research that highlighted the effectiveness of gradient-boosting techniques in other domains, suggesting that their capabilities may be less reliable when applied to the nuances of art auction data. While neural network architectures such as MLP, VIME, and DeepGBM theoretically have the capacity to capture complex interactions in high-dimensional data, they consistently underperformed compared to tree-based models. This outcome suggests that the specific characteristics of art auction data, such as the influence of external market factors and subjective elements like artist reputation and artwork provenance, are not effectively captured by these models. Neural networks tend to excel in larger datasets with abundant feature interactions; however, in the context of auction data, where the sample size is relatively small and features like ARTIST or TECHNIQUE dominate, tree-based models outperform due to their strong regularization ability. These results raise questions about the applicability of neural networks in auction price prediction and suggest that simpler, interpretable models may be more effective in this domain. One key finding of this study is that the inclusion of image-based features, such as Colorfulness and SVD entropy, did not substantially improve model performance. This contrasts with previous research in the price prediction domain, where image features have been shown to enhance predictions. In the art market, however, core metadata features like ARTIST, TOTAL DIMENSIONS, and YEAR were consistently more influential in driving model accuracy. The limited impact of image features in this study may be due to the highly subjective nature of art valuation, where non-visual factors such as provenance, historical significance, and artist reputation often outweigh visual characteristics in determining price. The implications of these findings raise concerns for stakeholders in the art market, including auction houses, investors, and analysts. Although tree-based models like Random Forest and XGBoost showed some degree of outperformance, their predictive capabilities still fall short of providing consistently accurate and transparent results. This suggests that relying on these models to optimize bidding strategies or guide investment decisions may not be as reliable as hoped. Furthermore, the feature importance analysis indicates that even traditional metadata, such as artist information, artwork dimensions, and auction histories, may not be sufficient to significantly improve predictive outcomes, highlighting the limitations of current data collection and curation practices in the art market. The underperformance of neural networks, particularly in the context of art auction prediction, may suggest that future research should focus less on deep learning approaches and more on refining tree-based methods. However, there remains potential for exploring hybrid models that integrate the strengths of both approaches, particularly in cases  
(SHAP) or Local Interpretable Model-Agnostic Explanations (LIME) could provide deeper insights into other models' behavior, deepening the understanding of the reasons behind the predictions. This is especially important in markets like art, where trust and transparency are paramount. In conclusion, this study underscores the persistent challenges of applying machine learning to art auction prediction.