dtoolkit.pipeline.Pipeline.predict_proba#
- Pipeline.predict_proba(X, **params)#
Transform the data, and apply predict_proba with the final estimator.
Call transform of each transformer in the pipeline. The transformed data are finally passed to the final estimator that calls predict_proba method. Only valid if the final estimator implements predict_proba.
- Parameters:
- Xiterable
Data to predict on. Must fulfill input requirements of first step of the pipeline.
- **paramsdict of str -> object
If enable_metadata_routing=False (default):
Parameters to the predict_proba called at the end of all transformations in the pipeline.
If enable_metadata_routing=True:
Parameters requested and accepted by steps. Each step must have requested certain metadata for these parameters to be forwarded to them.
New in version 0.20.
Changed in version 1.4: Parameters are now passed to the
transform
method of the intermediate steps as well, if requested, and if enable_metadata_routing=True.See Metadata Routing User Guide for more details.
- Returns:
- y_probandarray of shape (n_samples, n_classes)
Result of calling predict_proba on the final estimator.