Actions10
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- Data Actions
- Finance Actions
- Prediction Actions
- Report Actions
Prediction → Evaluate Prediction Model
AI-generatedSummary
Evaluate a prediction model by calculating evaluation metrics comparing actual observed values against predicted values from the input dataset.
Inputs
- actualColumn (required) — Column containing actual observed values for prediction evaluation.
- predictedColumn (required) — Column containing predicted values for prediction evaluation.
- advancedOptionsJson — Optional JSON object forwarded to the underlying domain operation; no formulas or code execution occurs here.
- allowPredictiveOperations — Boolean to explicitly enable predictive operations; default is false (disabled).
- currency — Optional currency metadata forwarded to finance/report/prediction related operations.
- forecastHorizon — Number of future points requested for predictive operations; defaults to 3.
- maxForecastHorizon — Maximum forecast horizon allowed; defaults to 12.
- maxRows — Maximum number of input rows the operation accepts; defaults to 1000.
Output shape
a single structured output containing evaluation metrics such as error measurements comparing the actual and predicted columns
The operation returns computed metrics for model evaluation; it accepts options influencing predictive behavior, row limits, and advanced underlying domain options. Predictive operations are disabled by default and must be enabled explicitly.
Examples
Example 1: Evaluate the accuracy of a financial prediction model
Provide the input dataset with columns specifying observed actual and predicted values, set 'actualColumn' and 'predictedColumn' accordingly, and optionally adjust forecast horizon and row limits.