# Tabular Data Models for Predicting Art Auction Results
## 3 Results
### sMAPE Score Analysis Across Datasets  
The analysis of sMAPE scores across the different datasets provides valuable insights into the predictive capabilities of the compared machine learning models. sMAPE is particularly useful in this context, as it measures the percentage difference between predicted \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Calculated Field** & **TOTAL-DIMENSIONS** & **YEAR** & **PRICE** \\ \hline Count & 25,408 & 25,408 & 25,408 \\ \hline Mean & 2259.03 & 1973.6 & 225.68 \\ \hline Standard deviation & 1674.0 & 21.7 & 505.40 \\ \hline Minimal value & 10.64 & 1900 & 1 \\ \hline
25\% Quantile & 875 & 1963 & 50 \\ \hline
50\% Quantile & 1750 & 1974 & 99 \\ \hline
75\% Quantile & 3401 & 1985 & 200 \\ \hline Maximal value & 10,000 & 2023 & 10,000 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Distribution of the numerical core features. and actual values, symmetrically penalizing both over- and under-predictions. This makes it especially suitable for art auction price predictions, where both types of errors can have a significant impact. Table 3 provides a comparative evaluation of the models based on their sMAPE scores across the datasets. In the NoImg dataset, XGBoost demonstrates the best performance, achieving a sMAPE score of 55.11%, with LightGBM following at 57.53%. The LinearModel, however, exhibits significantly inferior performance, with sMAPE over 100% for each dataset, indicating its limitations in handling the complexity of auction data. Similarly, DeepGBM with a sMAPE of 76.92% and VIME with 75.29% struggle to generalize effectively, while other models present moderate performance, ranging from 60.57% for SAINT to 68.50% for ModelTree. In the Color dataset, RandomForest emerges as the top performer with a sMAPE of 55.95%, outperforming XGBoost, which records 57.50%. Other models result in similar ranges of error, with improvement only for VIME, with almost 10 percentage points of decrease. In the SVD dataset, XGBoost once again leads with a sMAPE score of 54.83%, followed closely by RandomForest at 56.94%. Contrary to the Color dataset, providing SVD Entropy resulted in the worst noted error for VIME (86.44%) and DeepGBM (87.96%). For the ColorSVD dataset, RandomForest delivers the best performance, achieving a sMAPE of 55.51%, with CatBoost in second place at 58.25%. VIME (74.09%) and DeepGBM (76.18%) still exhibit higher error rates than other moderately performing models, suggesting that complex neural network-based models are less effective compared to ensemble methods when Colorfulness Score and SVD Entropy enhance the auction data. The Housing dataset is simpler compared to the auction datasets, as it displays higher linearity between features. In this setting, LightGBM achieves the best performance with a sMAPE of 14.71%, slightly outperforming XGBoost (14.84%). However, DeepGBM performs poorly in this benchmark, with a sMAPE of 35.13%, further demonstrating DeepGBM may not be suitable for this kind of tabular dataset, while this time, VIME is placed among the moderate performers. Overall, XGBoost and RandomForest consistently exhibit superior performance across the auction-related datasets; however, the prediction errors still exceed the threshold for what would be considered satisfactory results. The LinearModel consistently performs poorly, with sMAPE scores exceeding 100% in most auction datasets, underscoring its unsuitability for these tasks.