Create a Cross-Validation Report

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


Data mining was deprecated in SQL Server 2017 Analysis Services and now discontinued in SQL Server 2022 Analysis Services. Documentation is not updated for deprecated and discontinued features. To learn more, see Analysis Services backward compatibility.

This topic walks you through creation of a cross-validation report using the Accuracy Chart tab in Data Mining Designer. For general information about what a cross-validation report looks like, and the statistical measures it includes, see Cross-Validation (Analysis Services - Data Mining).

A cross-validation report is fundamentally different from an accuracy chart, such as a lift chart or classification matrix.

  • Cross-validation assesses the overall distribution of data that is used in a model or structure; therefore, you do not specify a testing data set. Cross-validation always uses only the original data that was used to train the model or the mining structure.

  • Cross-validation can only be performed with respect to a single predictable outcome. If the structure supports models that have different predictable attributes, you must create separate reports for each predictable output.

  • Only models that are related to the currently selected structure are available for cross-validation.

  • If the structure that is currently selected supports a combination of clustering and non-clustering models, when you click Get Results, the cross-validation stored procedure will automatically load models that have the same predicted column, and ignore clustering models that do not share the same predictable attribute.

  • You can create a cross-validation report on a clustering model that does not have a predictable attribute only if the mining structure does not support any other predictable attributes.

Select a mining structure

  1. Open the Data Mining Designer in SQL Server Data Tools.

  2. In Solution Explorer, open the database that contains the structure or model for which you want to create a report.

  3. Double-click the mining structure to open the structure and its related models in Data Mining Designer.

  4. Click the Mining Accuracy Chart tab.

  5. Click the Cross Validation tab.

Set cross-validation options

  1. On the Cross Validation tab, for Fold Count, click the down arrow to select a number between 1 and 10. The default value is 10.

    The Fold Count represents the number of partitions that will be created within the original data set. If you set Fold Count to 1, the training set will be used without partitioning.

  2. For Target Attribute, click the down arrow, and select a column from the list. If the model is a clustering model, select #Cluster to indicate that the model does not have a predictable attribute. Note that the value, #Cluster, is available only when the mining structure does not support other types of predictable attributes.

    You can select only one predictable attribute per report. By default, all related models that have the same predictable attribute are included in the report.

  3. For Max Cases, type a number that is large enough to provide a representative sample of data when the data is split among the specified number of folds. If the number is greater than the count of cases in the model training set, all cases will be used.

    If the training data set is very large, setting the value for Max Cases limits the total number of cases processed, and lets the report finish faster. However, you should not set Max Cases too low or there may be insufficient data for cross-validation.

  4. Optionally, for Target State, type the value of the predictable attribute that you want to model. For example, if the column [Bike Buyer] has two possible values, 1 (Yes) and 2 (No), you can enter the value 1 to assess the accuracy of the model for just the desired outcome.


    If you do not enter a value, the Target Threshold option is not available, and the model is assessed for all possible values of the predictable attribute.

  5. Optionally, for Target Threshold, type a decimal number between 0 and 1 to specify the minimum probability that a prediction must have to be counted as accurate.

    For additional tips about how to set probability thresholds, see Measures in the Cross-Validation Report.

  6. Click Get Results.

  1. Right-click the completed report on the Cross Validation tab.

  2. In the shortcut menu, select Print or Print Preview to review the report first.

Create a copy of the report in Microsoft Excel

  1. Right-click the completed report on the Cross Validation tab.

  2. In the shortcut menu, select Select All.

  3. Right-click the selected text, and select Copy.

  4. Paste the selection into an open Excel workbook. If you use the Paste option, the report is pasted into Excel as HTML, which preserves row and column formatting. If you paste the report by using the Paste Special options for text or Unicode text, the report is pasted in row-delimited format.

See Also

Measures in the Cross-Validation Report