Towards Stable Machine Learning Model Retraining via Slowly Varying Sequences (2403.19871v5)

Abstract: We consider the problem of retraining ML models when new batches of data become available. Existing approaches greedily optimize for predictive power independently at each batch, without considering the stability of the model's structure or analytical insights across retraining iterations. We propose a model-agnostic framework for finding sequences of models that are stable across retraining iterations. We develop a mixed-integer optimization formulation that is guaranteed to recover Pareto optimal models (in terms of the predictive power-stability trade-off) with good generalization properties, as well as an efficient polynomial-time algorithm that performs well in practice. We focus on retaining consistent analytical insights-which is important to model interpretability, ease of implementation, and fostering trust with users-by using custom-defined distance metrics that can be directly incorporated into the optimization problem. We evaluate our framework across models (regression, decision trees, boosted trees, and neural networks) and application domains (healthcare, vision, and language), including deployment in a production pipeline at a major US hospital. We find that, on average, a 2% reduction in predictive power leads to a 30% improvement in stability.

• The paper introduces a mixed-integer optimization framework that balances prediction error with model stability across retrainings.
• It reformulates model selection as a shortest-path problem, ensuring consistent analytical insights while limiting performance degradation.
• Empirical validation on a hospital dataset shows minimized intra-sequence variations, crucial for applications like mortality risk prediction.

An Analysis of Improving Model Stability Across Retrainings with Slowly Varying Sequences

Introduction to Model Retraining Challenges

Machine learning models, once deployed, often require updates to incorporate new data and maintain performance. Typically, this process has focused on selecting the best-performing model for new data—leading to potentially significant shifts in model structures and, consequently, the insights they generate. Such changes can erode trust among users, especially in high-stake fields like healthcare, where consistent decision-making is paramount. Recognizing this, the work by Vassilis Digalakis Jr, Yu Ma, Phevos Paschalidis, and Dimitris Bertsimas introduces a novel approach focusing on model stability across retrainings, aiming at retaining consistent analytical insights alongside model performance.

The Stability-Oriented Retraining Framework

Their method employs a mixed integer optimization algorithm designed to train machine learning models across different data batch updates. The core innovation of this approach lies in its ability to maintain consistency in the model's analytical insights—crucial for interpretability, implementation ease, and fostering user trust—through custom-defined distance metrics integrated directly into the optimization problem. The results from a real-world case paper indicate that this method achieves enhanced stability over traditional, greedily trained models, with only a minor and controllable sacrifice in model performance.

Theoretical Backdrop and Methodology

At the heart of their approach is the formulation as a mixed-integer optimization problem, which balances two objectives: minimizing predictive error and ensuring model stability. This equilibrium is maintained by controlling the algorithm's tolerance for suboptimal performance in favor of structural stability. In practical terms, they propose a method where, instead of retraining a model from scratch for every new batch of data, one selects from a pool of pre-trained candidate models. This selection process is guided by a stability metric, ensuring smooth transitions between model iterations and maintaining a high level of interpretability.

Detailed Implementation Insights

The innovation extends to the method's tractability, facilitated by a reduction of the model selection problem to a shortest path problem, which is solvable in polynomial time. This theoretical framework allows for the application of their methodology across a wide range of machine learning model types, from simple linear models to more complex tree-based methods. The applicability is further underscored by their comprehensive distance calculation methods, catering to structural differences and shifts in feature importance—a testament to the method's flexibility and depth.

Empirical Validation and Results

Utilizing datasets from the largest hospital system in Connecticut, the authors put their methodology to the test in the context of mortality risk prediction. The empirical findings underscore the methodology’s capability to minimize intra-sequence model variations without compromising the accuracy significantly. This result is pivotal for applications where consistent interpretation of model outputs is critical. Additionally, their experiments reveal an optimal data retraining frequency, further demonstrating the practical considerations underlying their methodological choices.

Future Prospects and Considerations

Despite its promising results, the methodology's practical implementation raises concerns around computational demands, especially in selecting medically viable candidate models. This caveat suggests a potential area for refining the algorithm’s efficiency or considering subsets of candidate models. Moreover, the method’s adaptability to varying data update frequencies remains to be fully explored, representing another avenue for future research.

Concluding Thoughts

In conclusion, the paper by Digalakis Jr et al. marks a significant step toward stabilizing machine learning model structures through retrainings. Their methodology not only addresses a critical gap in the continuous deployment of machine learning models but also provides a scalable solution adaptable to various model types and application areas. Although challenged by computational demands and the dynamic nature of data updates, the proposed framework opens new pathways for developing reliable, interpretable, and stable machine learning applications.