Automatic Generation of Attention Rules For Containment of Machine Learning Model Errors
This work addresses the challenge of making ML solutions business-grade by improving model diagnosis and error containment, though it appears incremental as it builds on existing concepts of attention sets and feature-based slicing.
The paper tackles the problem of maintaining low error rates in machine learning models by proposing algorithms to automatically generate attention rules that separate observations likely to be predicted incorrectly, showing that these strategies outperform common baselines like threshold-based selection on publicly-available datasets.
Machine learning (ML) solutions are prevalent in many applications. However, many challenges exist in making these solutions business-grade. For instance, maintaining the error rate of the underlying ML models at an acceptably low level. Typically, the true relationship between feature inputs and the target feature to be predicted is uncertain, and hence statistical in nature. The approach we propose is to separate the observations that are the most likely to be predicted incorrectly into 'attention sets'. These can directly aid model diagnosis and improvement, and be used to decide on alternative courses of action for these problematic observations. We present several algorithms (`strategies') for determining optimal rules to separate these observations. In particular, we prefer strategies that use feature-based slicing because they are human-interpretable, model-agnostic, and require minimal supplementary inputs or knowledge. In addition, we show that these strategies outperform several common baselines, such as selecting observations with prediction confidence below a threshold. To evaluate strategies, we introduce metrics to measure various desired qualities, such as their performance, stability, and generalizability to unseen data; the strategies are evaluated on several publicly-available datasets. We use TOPSIS, a Multiple Criteria Decision Making method, to aggregate these metrics into a single quality score for each strategy, to allow comparison.