  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.
