The Data Addition Dilemma (2408.04154v1)

Abstract: In many machine learning for healthcare tasks, standard datasets are constructed by amassing data across many, often fundamentally dissimilar, sources. But when does adding more data help, and when does it hinder progress on desired model outcomes in real-world settings? We identify this situation as the \textit{Data Addition Dilemma}, demonstrating that adding training data in this multi-source scaling context can at times result in reduced overall accuracy, uncertain fairness outcomes, and reduced worst-subgroup performance. We find that this possibly arises from an empirically observed trade-off between model performance improvements due to data scaling and model deterioration from distribution shift. We thus establish baseline strategies for navigating this dilemma, introducing distribution shift heuristics to guide decision-making on which data sources to add in data scaling, in order to yield the expected model performance improvements. We conclude with a discussion of the required considerations for data collection and suggestions for studying data composition and scale in the age of increasingly larger models.

• The paper formalizes the 'Data Addition Dilemma,' showing that adding multi-source data can paradoxically decrease model performance due to distribution shifts.
• The study uses real-world hospital datasets and models like LSTM to reveal trade-offs between increased training data and accuracy declines.
• The authors propose practical heuristics to guide data addition, offering strategies to optimize training data composition in healthcare settings.

The Data Addition Dilemma: An Expert Overview

In the development of machine learning models for healthcare, the size and composition of training datasets play a critical role in determining model performance. The paper "The Data Addition Dilemma" by Shen, Raji, and Chen explores a crucial aspect of dataset management—whether adding more data across dissimilar sources benefits or hinders model outcomes. This work identifies and formalizes the "Data Addition Dilemma," offering insights into the complexities underlying multi-source data scaling.

Key Contributions

The authors address the challenge of determining when the inclusion of additional data sources will enhance model performance. They identify a trade-off between improvements in model performance due to increased dataset size and potential deteriorations resulting from distribution shifts. The paper's significant contributions can be summarized as follows:

1. Problem Formalization:
   • The work introduces the "Data Addition Dilemma," highlighting scenarios where an increase in training data size through the inclusion of multiple sources can paradoxically lead to degraded model performance.
   • The dilemma is formalized to demonstrate why increased dataset size does not always guarantee better performance, especially under dynamic distribution changes.
2. Exploration of Data Composition:
   • The authors theoretically show how changes in data composition from multi-source scaling can negatively impact model performance.
   • They analyze various distribution shift measures, demonstrating how such shifts in single-source and multi-source data addition contexts influence performance patterns.
3. Strategies for Data Addition:
   • The paper proposes heuristics to determine when adding more data is beneficial.
   • It presents a performance analysis from real-world hospital datasets, illustrating how to navigate the Data Addition Dilemma by selecting additional data sources likely to yield performance improvements.

Experimental Insights

The authors conducted extensive experiments using the eICU Collaborative Research Database, evaluating the impact of adding training data from various hospital sources on model performance. They tested three models: Logistic Regression (LR), Light Gradient Boosting Machine (LGBM), and Long Short-Term Memory (LSTM) networks. Their experimental setup involved increasing the training set size by adding data from multiple hospitals and assessing the effect on a reference test set.

Several key findings emerged from their experiments:

• Impact of Single Source Distribution Shift: Training on one hospital and testing on another can lead to significant changes in AUC performance, which often deteriorates due to distributional differences.
  • They found high correlations in performance drops across different model architectures, indicating that distribution shifts affect various models similarly.
• Data Addition Patterns: Adding data from another hospital to the training set can both improve or degrade AUC performance on the reference test set.
  • The observed performance patterns when mixing data sources were significantly correlated with single-source out-of-distribution performance changes, suggesting that understanding single-source shifts can inform multi-source data addition.
• Correlation with Distribution Metrics: The paper shows that certain distribution metrics, specifically heuristic scores based on distribution shift measures (like KL divergence and expected predictor scores), are strongly correlated with AUC drops.
  • The empirical results confirmed that these heuristics could guide decisions on which additional data sources to include.

Practical and Theoretical Implications

The implications of this paper are twofold:

1. Practical:
   • The insights provided can inform better dataset construction strategies in clinical machine learning settings, ensuring that the addition of data improves rather than hinders model performance.
   • The heuristics developed offer practical tools for data scientists and clinicians to make informed decisions about data scaling, fostering more robust and fair models.
2. Theoretical:
   • The paper advances the understanding of how data composition and distribution shifts impact model performance, providing a foundation for future research into data-centric model improvements.
   • The formalization and analysis offered in this work pave the way for more nuanced studies on dataset scaling laws and their effect on model outcomes.

Future Directions

Building on their findings, the authors suggest several future research avenues:

• Broader Application:
  • Extending the paper to various healthcare tasks such as treatment modeling, risk stratification, and patient subtyping to validate the generalizability of the heuristics beyond ICU mortality prediction.
• Enhanced Data Accumulation Models:
  • Investigating more complex data accumulation strategies, including combinations of multiple sources, and exploring different data-centric interventions like data pruning or synthetic data generation.
• Deep Theoretical Insights:
  • Further theoretical work to solidify the foundations of meaningful data practices, including robustness guarantees and distributional analyses, could provide stronger assurances for dataset decision-making in applied settings.

In conclusion, "The Data Addition Dilemma" provides a rigorous and insightful exploration of the impacts of multi-source data scaling in machine learning for healthcare. The paper bridges theoretical analysis with practical heuristic development, offering valuable guidance for improving model outcomes through strategic data management.