An entity serves as a collection of semantically related features and labels. Users define entities to map to the domain of their specific use cases. For instance, in the context of a ride-hailing service, entities could include customers and drivers, grouping related features and labels associated with these respective entities.
featureform
library provides an entity
decorator that can be applied to classes. This decorator allows you to associate features and labels with the entity using Featureform data sets that you’ve defined earlier. A feature typically comprises at least two columns: an entity column (similar to an index or primary key) and a value column. Optionally, it can include a third column for timestamps, which becomes essential for constructing point-in-time correct training sets.
For example, consider a “User” entity with an “age” feature and a “credit score” label associated with it:
features
method of a Featureform Client object.