To build computational graphs, you need to specify an
estimator, a (set of) indexer(s), and any preprocessing pipelines wanted.
The indexer(s) determine the cross validation strategy to use during
transform calls. The
be used if no cross-validation is wanted.
The basic node is the
Learner. The learner contains the sub-graph pertaining to a specific indexer-estimator-preprocessing configuration.
Transformer contains the sub-graph of a specific indexer-preprocessing configuration. To learn more, see the
learner mechanics tutorial.
To build a graph of a set of
Transformer instances, several handles come in handy for ensuring efficient computation.
Group class is a handle for a set of learners and transformers sharing a specific indexer, while the
Layer class is a handle for
a set of groups. To learn more about how to efficiently parallelize independent estimations, see the layer mechanics tutorial.
Frequently, we want the output of some set of learners to be feed as input to some other set. The
ParallelProcessing engine is a purpose-built
parallel job-execution engine design to handle these types of scenarios. Together with the
Sequential handle, designing
deep computational graphs is straightforward. See the advanced graph mechanics tutorial for more information.