Model Slicing: A Data Engineering Perspective

Model slicing is a machine learning diagnostic method that partitions data into coherent subgroups (slices), such as those corresponding to protected attributes, and computes model performance metrics within each slice. As an example, model slicing might find that a healthcare model performs well overall but poorly for children and for seniors. We present a tutorial that describes model slicing from a data engineering perspective, as multi-dimensional analytics of model performance data. Within this formulation, we represent variants of model slicing in terms of their dimension and measure attributes. We also discuss performance optimizations and we conclude with directions for future work.