SystemGetAccuracyResults (Analysis Services - Data Mining)

Applies to: SQL Server 2019 and earlier Analysis Services Azure Analysis Services Fabric/Power BI Premium

Returns cross-validation accuracy metrics for a mining structure and all related models, excluding clustering models.

This stored procedure returns metrics for the whole data set as a single partition. To partition the dataset into cross-sections and return metrics for each partition, use SystemGetCrossValidationResults (Analysis Services - Data Mining).

Syntax

  
SystemGetAccuracyResults(<mining structure>,   
[,<mining model list>]  
,<data set>  
,<target attribute>  
[,<target state>]  
[,<target threshold>]  
[,<test list>])  

Arguments

mining structure
Name of a mining structure in the current database.

(Required)

model list
Comma-separated list of models to validate.

The default is null. This means that all applicable models are used. When the default is used, clustering models are automatically excluded from the list of candidates for processing.

(Optional)

data set
A integer value that indicates which partition in the mining structure is used for testing. The value is derived from a bitmask that represents the sum of the following values, where any single value is optional:

For a complete list of possible values, see the Remarks section of this topic.

(required)

target attribute
String that contains the name of a predictable object. A predictable object can be a column, nested table column, or nested table key column of a mining model.

(required)

target state
String that contains a specific value to predict.

If a value is specified, the metrics are collected for that specific state.

If no value is specified, or if null is specified, the metrics are computed for the most probable state for each prediction.

The default is null.

(optional)

target threshold
Number between 0.0 and 1 that specifies the minimum probability in which the prediction value is counted as correct.

The default is null, which means that all predictions are counted as correct.

(optional)

test list
A string that specifies testing options. This parameter is reserved for future use.

(optional)

Return Type

The rowset that is returned contains scores for each partition and aggregates for all models.

The following table lists the columns returned by GetValidationResults.

Remarks

The following table provides examples of the values that you can use to specify the data in the mining structure that is used for cross-validation. If you want to use test cases for cross-validation, the mining structure must already contain a testing data set. For information about how to define a testing data set when you create a mining structure, see Training and Testing Data Sets.

For more information about the scenarios in which you would use cross-validation, see Testing and Validation (Data Mining).

Examples

This example returns accuracy measures for a single decision tree model, v Target Mail DT, that is associated with the vTargetMail mining structure. The code on line four indicates that the results should be based on the testing cases, filtered for each model by the filter specific to that model. [Bike Buyer] specifies the column that is to be predicted, and the 1 on the following line indicates that the model is to be evaluated only for the specific value 1, meaning "Yes, will buy".

The final line of the code specifies that the state threshold value is 0.5. This means that predictions that have a probability greater than 50 percent should be counted as "good" predictions when calculating accuracy.

CALL SystemGetAccuracyResults (  
[vTargetMail],  
[vTargetMail DT],  
6,  
'Bike Buyer',  
1,  
0.5  
)  

Sample Results:

Requirements

Cross-validation is available only in SQL Server Enterprise beginning with SQL Server 2008.

See Also

SystemGetCrossValidationResults (Analysis Services - Data Mining)
SystemGetAccuracyResults
SystemGetClusterCrossValidationResults (Analysis Services - Data Mining)
SystemGetClusterAccuracyResults (Analysis Services - Data Mining)