Latest Research From Stanford Introduces ‘Domino’: A Python Tool for Identifying and Describing Underperforming Slices in Machine Learning Models

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Machine learning and Artificial Intelligence models have gained promising results in recent years. The major factor behind their success is the availability and development of vast datasets. However, regardless of how many terabytes of data you have or how skilled you are at data science, machine learning models will be useless and even dangerous if you can’t make sense of data records.

A slice is a collection of data samples with a common feature. For example, in a picture dataset, photographs of antique vehicles make up a slice. When a model’s performance on the data samples in a slice is significantly lower than its overall performance, the slice is considered underperforming.