bigframes.ml.forecasting.ARIMAPlus.score#
- ARIMAPlus.score(X: DataFrame | Series | DataFrame | Series, y: DataFrame | Series | DataFrame | Series, id_col: DataFrame | Series | DataFrame | Series | None = None) DataFrame[source]#
Calculate evaluation metrics of the model.
Note
Output matches that of the BigQuery ML.EVALUATE function. See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-evaluate#time_series_models for the outputs relevant to this model type.
- Parameters:
bigframes.series.Series (y (bigframes.dataframe.DataFrame or)
pandas.core.series.Series) (or pandas.core.frame.DataFrame or) – A dataframe or series only contains 1 column as evaluation timestamp. The timestamp must be within the horizon of the model, which by default is 1000 data points.
bigframes.series.Series
pandas.core.series.Series) – A dataframe or series only contains 1 column as evaluation numeric values.
(Optional[bigframes.dataframe.DataFrame] (id_col)
Optional[bigframes.series.Series] (or)
Optional[pandas.core.frame.DataFrame] (or)
Optional[pandas.core.series.Series] (or)
None (or) – An optional dataframe or series contains at least 1 column as evaluation id column.
None) (default) – An optional dataframe or series contains at least 1 column as evaluation id column.
- Returns:
A DataFrame as evaluation result.
- Return type: