pipefitter.pipeline.PipelineModel.score¶
-
PipelineModel.
score
(table, **kwargs)¶ Apply transformations and score the data using the trained model
Parameters: table : data set
A data set that is of the same type as the training data set
Returns: pandas.DataFrame
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) >>> score = model.score(data)