Pipeline.score(population_table: Union[,,], peripheral_tables: Optional[Union[Dict[str, Union[,]], Sequence[Union[,]]]] = None) → getml.pipeline.scores_container.Scores[source]

Calculates the performance of the predictor.

Returns different scores calculated on population_table and peripheral_tables.

population_table (DataFrame, View or Subset):

Main table containing the target variable(s) and corresponding to the population Placeholder instance variable.

peripheral_tables (List[DataFrame or View], dict, DataFrame or View, optional):

Additional tables corresponding to the peripheral Placeholder instance variable. If passed as a list, the order needs to match the order of the corresponding placeholders passed to peripheral.

If you pass a Subset to population_table, the peripheral tables from that subset will be used. If you use a Container, StarSchema or TimeSeries, that means you are passing a Subset.


Only fitted pipelines (fit()) can be scored.