Working with Metadata: Tags and More
Scikit-learn uses metadata, such as estimator tags, to control how models behave in various contexts including cross-validation and pipeline processing, and their capabilities like supported output types. Additionally, tags can provide information about an estimator such as whether it can handle multi-output data or missing values, enabling scikit-learn to optimize workflows dynamically.
scikit-learn’s metadata captures information related to model inputs and outputs and then typically uses this information to control the flow of data between different tasks in a Pipeline. Metadata objects come in two varieties, routers and consumers, where routers move metadata to consumers and consumers use that metadata in their calculations. This is known as Metadata Routing in scikit-learn.
More on metadata routing
Metadata routing in scikit-learn is a feature that allows users to control how metadata is passed between router and consumer objects in a...
