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Pipeline(steps: typing.List[typing.Tuple[str, bigframes.ml.base.BaseEstimator]])Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator.
Intermediate steps of the pipeline must be transforms. That is, they
must implement fit and transform methods.
The final estimator only needs to implement fit.
The purpose of the pipeline is to assemble several steps that can be
cross-validated together while setting different parameters. This simplifies code and allows for
deploying an estimator and preprocessing together, e.g. with Pipeline.to_gbq(...).
Methods
Pipeline
Pipeline(steps: typing.List[typing.Tuple[str, bigframes.ml.base.BaseEstimator]])Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator.
Intermediate steps of the pipeline must be transforms. That is, they
must implement fit and transform methods.
The final estimator only needs to implement fit.
The purpose of the pipeline is to assemble several steps that can be
cross-validated together while setting different parameters. This simplifies code and allows for
deploying an estimator and preprocessing together, e.g. with Pipeline.to_gbq(...).
__init__
__init__(steps)API documentation for __init__ method.
fit
fit(X, y=None)Fit the model.
Fit all the transformers one after the other and transform the data. Finally, fit the transformed data using the final estimator.
| Returns | |
|---|---|
| Type | Description |
Pipeline |
Pipeline with fitted steps. |
get_params
get_params(deep=True)Get parameters for this estimator.
| Parameter | |
|---|---|
| Name | Description |
deep |
bool, default True
Default |
| Returns | |
|---|---|
| Type | Description |
Dictionary |
A dictionary of parameter names mapped to their values. |
predict
predict(X)API documentation for predict method.
score
score(X, y=None)API documentation for score method.
to_gbq
to_gbq(model_name, replace=False)Save the pipeline to BigQuery.
| Returns | |
|---|---|
| Type | Description |
Pipeline |
Saved model(pipeline). |