Aggregating predictive metrics

One of the most powerful features of integrating data mining models with a business intelligence platform is the ability to drill up and down through layers of data. For the predictive results to make sense, you need to specify the aggregation function to be used. Therefore, the Model Import dialog box allows you to select the appropriate aggregation function.

Choosing the proper aggregation function requires some knowledge about how the model behaves. For example,

• If the predictive metric generates a score that is a zero or a one, use Sum to calculate the number of "one" scores.

• If the predictive metric generates a "linear" output, like "Forecasted Revenue," usually from a regression predictor, use Sum to roll up the predictive results.

• If the predictive metric generates a confidence or percentage, use Average to calculate the mean confidence.

• If the predictive metric generates a numeric classifier, like a cluster/segment number, use Mode to calculate the most common classifier.

• For models with outputs that cannot be aggregated, select "None" as the aggregation function.

For a complete list of available aggregation functions, see the List of available aggregation functions.

The recommended aggregation functions for both the score and confidence predictive metrics are automatically provided by this dialog, based on the type of model being imported.

Related Topics

• Importing the model into MicroStrategy

• List of available aggregation functions

• Using the Import Data Mining Model dialog box

• Using Data Mining Services