Článek
05/23/2011

SystemGetAccuracyResults (Analysis Services - Data Mining)

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).

Note

This stored procedure is not supported for models that are built by using the Microsoft Time Series algorithm or the Microsoft Sequence Clustering algorithm. Also, for clustering models, use the separate stored procedure, SystemGetClusterAccuracyResults (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:

Training cases

0x0001

Test cases

0x0002

Model filter

0x0004

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.

Column Name	Description
Model	The name of the model that was tested. All indicates that the result is an aggregate for all models.
AttributeName	The name of the predictable column.
AttributeState	A target value in the predictable column. If this column contains a value, metrics are collected for the specified state only. If this value is not specified, or is null, the metrics are computed for the most probable state for each prediction.
PartitionIndex	Denotes the partition to which the result applies. For this procedure, always 0.
PartitionCases	An integer that indicates the number of rows in the case set, based on the <data set> parameter.
Test	The type of test that was performed.
Measure	The name of the measure returned by the test. Measures for each model depend on the model type, and the type of the predictable value. For a list of measures returned for each predictable type, see Cross-Validation Report (Analysis Services - Data Mining). For a definition of each measure, see Cross-Validation (Analysis Services - Data Mining).
Value	The value for the specified measure.

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 Partitioning Data into Training and Testing Sets (Analysis Services - Data Mining).

Integer Value	Description
1	Only training cases are used.
2	Only test cases are used.
3	Both the training cases and testing cases are used.
4	Invalid combination.
5	Only training cases are used, and the model filter is applied.
6	Only test cases are used, and the model filter is applied.
7	Both the training and testing cases are used, and the model filter is applied.

For more information about the scenarios in which you would use cross-validation, see Validating Data Mining Models (Analysis Services - 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:

ModelName	AttributeName	AttributeState	PartitionSize	Test	Measure	Value
v Target Mail DT	Bike Buyer	1	1638	Classification	True Positive	605
v Target Mail DT	Bike Buyer	1	1638	Classification	False Positive	177
v Target Mail DT	Bike Buyer	1	1638	Classification	True Negative	501
v Target Mail DT	Bike Buyer	1	1638	Classification	False Negative	355
v Target Mail DT	Bike Buyer	1	1638	Likelihood	Log Score	-0.598454638753028
v Target Mail DT	Bike Buyer	1	1638	Likelihood	Lift	0.0936717116894395
v Target Mail DT	Bike Buyer	1	1638	Likelihood	Root Mean Square Error	0.361630800104946

Requirements

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

Sdílet prostřednictvím