Tabular data models for predicting art auction results

HIGHLIGHTS

• What: 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. The authors 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 the authors provide insights into their relative effectiveness across different scenarios. This study provides guidance for auction houses art investors and market analysts in refining predictive approaches identifying key challenges and . . .
  

   

  

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