pipefitter.pipeline.PipelineModel.transform¶
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PipelineModel.
transform
(table)¶ Run the transforms in the trained pipeline
Parameters: table : data set
A data set that is of the same type as the training data set
Returns: data set
A data set of the same type that was passed in table
Examples
Basic pipeline model transform using imputers and an estimator:
>>> mean_imp = Imputer(Imputer.MEAN) >>> mode_imp = Imputer(Imputer.MODE) >>> dtree = DecisionTree(target='Origin', ... nominals=['Type', 'Cylinders', 'Origin'], ... inputs=['MPG_City', 'MPG_Highway', 'Length', ... 'Weight', 'Type', 'Cylinders']) >>> pipe = Pipeline([mean_imp, mode_imp, dtree]) >>> model = pipe.fit(training_data) >>> new_table = model.transform(data)