Pipeline¶
Applies one model on the output of another
Documentation¶
Creates a sequence of data transformers with an optional final predictor.
Intermediate steps of the pipeline must be transformers, that is, they must implement fit and transform methods. The final estimator only needs to implement fit. The transformers in the pipeline can be cached using memory argument. The pipline serves multiple purposes:
It assembles several steps that can be cross-validated together while setting different parameters.
Only one fit/predict call for the whole sequence.
Avoid statistics leaking from test to train in cross-validation.
Definition¶
Input ports¶
- models
Output ports¶
- out-model
Configuration¶
- Flatten (flatten)
Flattens multiple pipeline objects into a single pipeline containing all models
- Model names (names)
Comma separated list of model names, eg. Rescale, SVC. If fewer names are given than models then default names will be used.
Examples¶
The node can be found in:
Implementation¶
- class node_pipeline.Pipeline[source]
