dtoolkit.pipeline.Pipeline.predict_log_proba

Pipeline.predict_log_proba(X, **params)

Transform the data, and apply predict_log_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_log_proba method. Only valid if the final estimator implements predict_log_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_log_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_log_probandarray of shape (n_samples, n_classes)

Result of calling predict_log_proba on the final estimator.