Techniques for Programmatic Labeling in Machine Learning
In machine learning, the accurate labeling of data is crucial for training effective models. Data labeling involves assigning meaningful categories or classes to data instances, and while traditionally a human-driven process, there are various programmatic approaches to dataset labeling. This chapter delves into the following methods of programmatic data labeling in machine learning:
- Pattern matching
- Database (DB) lookup
- Boolean flags
- Weak supervision
- Semi-weak supervision
- Slicing functions
- Active learning
- Transfer learning
- Semi-supervised learning
