<model_name>!SHOW_EVALUATION_METRICS¶

Syntax¶

You can call this method to retrieve the cross-validation metrics generated when the model was trained, or you can call it with additional data that was not available at training time (out-of-sample data) and receive metrics based on how well the model predicts that data.

Return time-series cross-validation metrics generated at training time:

These metrics are available only if evaluate=TRUE in the CONFIG_OBJECT during model construction (this is the default).

<model_name>!SHOW_EVALUATION_METRICS();

Compute cross-validation metrics on additional out-of-sample data:

<model_name>!SHOW_EVALUATION_METRICS(
  INPUT_DATA => <input_data>,
  [ SERIES_COLNAME => '<series_colname>', ]
  TIMESTAMP_COLNAME => '<timestamp_colname>',
  TARGET_COLNAME => '<target_colname>',
  LABEL_COLNAME => '<label_column_name>',
  [ CONFIG_OBJECT => <config_object> ]
);

Arguments¶

The following arguments apply only to the additional out-of-sample data use case.

Required:

Not all of the following arguments are required for every use case.

INPUT_DATA => input_data

A reference to a table, view, or query that contains the future timestamps and values of the target and any exogenous variables used during training. Columns are matched between this argument and the original exogenous training data by name.

TIMESTAMP_COLNAME => 'timestamp_colname'

Name of the column containing the timestamps in input_data.

TARGET_COLNAME => 'target_colname'

Name of the column containing the target (dependent value) in input_data.

LABEL_COLNAME => 'label_column_name'

Name of the column containing the labels for the data. Labels are Boolean (true/false) values indicating whether a given row is a known anomaly. If you do not have labeled data, pass an empty string ('') for this argument.

Optional:

SERIES_COLNAME => 'series_colname'

Name of the column in input_data specifying the series.

CONFIG_OBJECT => config_object

An OBJECT containing key-value pairs used to configure the evaluation job.

Key	Type	Default	Description
prediction_interval	FLOAT	0.99	A value greater than or equal to 0.0 and less than 1.0. The default value of 0.95 means 95% of future points are expected to fall within the interval [lower_bound, upper_bound] derived from the forecast result.
on_error	STRING	'ABORT'	String (constant) specifying the error handling method. This is most useful when forecasting multiple series. Supported values are: 'abort': Abort the model forecasting operation if an error is encountered in any time series. 'skip': Skip any time series where forecasting encounters an error. This allows forecasting to succeed for other time series. Series that fail are absent from the model output.

Output¶