In case if we can avoid certain stages used during training while predicting we need to propagate
some changes to the model (eg. unscale weights or remove intercept). Also useful for extending summary
blocks (eg. during evaluation/cross-validation).
This class is used as a typical pipeline stage while training (fits and applies transformer, then calls the
nested estimator), but it automatically eliminates itself from the resulting model by applying model transformer.
Linear Supertypes
SummarizableEstimator[M], Estimator[M], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
In case if we can avoid certain stages used during training while predicting we need to propagate some changes to the model (eg. unscale weights or remove intercept). Also useful for extending summary blocks (eg. during evaluation/cross-validation).
This class is used as a typical pipeline stage while training (fits and applies transformer, then calls the nested estimator), but it automatically eliminates itself from the resulting model by applying model transformer.