Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
📰 ArXiv cs.AI
Evaluating tabular foundation models using proper scoring rules for probabilistic predictions
Action Steps
- Identify the limitations of traditional point-estimate metrics (RMSE, $R^2$) in evaluating tabular foundation models
- Supplement standard benchmarks with proper scoring rules to assess the quality of predicted distributions
- Implement proper scoring rules, such as log score or continuous ranked probability score, to evaluate probabilistic predictions
- Compare the performance of different tabular foundation models using proper scoring rules
Who Needs to Know This
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
Key Insight
💡 Proper scoring rules can effectively evaluate the quality of predicted distributions in tabular foundation models
Share This
📊 Evaluating tabular foundation models? Move beyond point-estimate metrics! 🤖
Key Takeaways
Evaluating tabular foundation models using proper scoring rules for probabilistic predictions
Full Article
Title: Distributional Regression with Tabular Foundation Models: Evaluating Probabilistic Predictions via Proper Scoring Rules
Abstract:
arXiv:2603.08206v4 Announce Type: replace-cross Abstract: Tabular foundation models such as TabPFN and TabICL already produce full predictive distributions, yet the benchmarks used to evaluate them (TabArena, TALENT, and others) still rely almost exclusively on point-estimate metrics (RMSE, $R^2$). This mismatch implicitly rewards models that elicit a good conditional mean while ignoring the quality of the predicted distribution. We make two contributions. First, we propose supplementing standar
Abstract:
arXiv:2603.08206v4 Announce Type: replace-cross Abstract: Tabular foundation models such as TabPFN and TabICL already produce full predictive distributions, yet the benchmarks used to evaluate them (TabArena, TALENT, and others) still rely almost exclusively on point-estimate metrics (RMSE, $R^2$). This mismatch implicitly rewards models that elicit a good conditional mean while ignoring the quality of the predicted distribution. We make two contributions. First, we propose supplementing standar
DeepCamp AI