Slicing functions

Slicing functions are functions that operate on data instances and produce binary labels based on specific conditions. Unlike traditional labeling functions that provide labels for the entire dataset, slicing functions are designed to focus on specific subsets of the data. These subsets, or slices, can be defined based on various features, patterns, or characteristics of the data. Slicing functions offer a fine-grained approach to labeling, enabling more targeted and precise labeling of data instances.

Slicing functions play a crucial role in weak supervision approaches, where multiple labeling sources are leveraged to assign approximate labels. Slicing functions complement other labeling techniques, such as rule-based systems or crowdsourcing, by capturing specific patterns or subsets of the data that may be challenging to label accurately using other methods. By applying slicing functions to the data, practitioners can exploit domain knowledge or specific data...