Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules

1. 1Identify the limitations of traditional point-estimate metrics (RMSE, $R^2$) in evaluating tabular foundation models
2. 2Supplement standard benchmarks with proper scoring rules to assess the quality of predicted distributions
3. 3Implement proper scoring rules, such as log score or continuous ranked probability score, to evaluate probabilistic predictions
4. 4Compare the performance of different tabular foundation models using proper scoring rules

Data scientists and AI engineers working with tabular foundation models can benefit from this research to improve the evaluation of their models, and product managers can use these insights to make informed decisions about model deployment

💡 Proper scoring rules can effectively evaluate the quality of predicted distributions in tabular foundation models

📊 Evaluating tabular foundation models? Move beyond point-estimate metrics! 🤖