Step 1: Build the Prediction Models

Before you can build a Prediction model, you must do the following:

• Import Web log data and runtime data into the Data Warehouse using the Commerce Server DTS tasks.

  For instructions for quickly importing data, see Data Warehouse Import Wizard. Or, to import sample data, follow the instructions in Preparing to Use the Tutorial in the Predictor Resource Tutorial.

• Create a model configuration for your task. For instructions, see Setting Up Model Configurations.

After you have performed this two steps, you are ready to build the prediction models.

To build the Prediction models

1. Expand Commerce Server Manager, expand Global Resources, expand Predictor on <server name>, expand Predictor Service, and then click Model Configurations.
2. In the details screen, right-click the model configuration you want to use to build an analysis model, and then click Build.
3. In the Model Build Properties dialog box, do the following:

   Use this	To do this
   Name	Type a name for the analysis model.
   Model type	Select prediction from the drop-down list.

4. Click Next.
5. In the second screen of the Model Build Properties dialog box, do the following:

   Use this	To do this
   Sample size	Type the number of cases that are used to build the analysis model. You can change the default value if necessary. For example, if you have a large table with 200,000 cases, you may want to specify that the Predictor resource use only 20,000 cases to build the analysis model. The default value is either –1 or 20,000: -1 means that all data is used if the total number of cases is less than 20,000 20,000 specifies that 20,000 cases are used because the total number of cases is larger than or equal to 20,000.
   Measured accuracy sample fraction	Type the fraction of the sample data you want to use to automatically score the accuracy of the model, as a number between 0.0 and 1.0. For example, if you type 0.0 as the value of the Measured accuracy sample fraction option, the model will not be scored. If you type 0.4, 40 percent of the sample data will be used to score the model. (The remaining 60 percent will be used to build the model.)
   Measured accuracy maximum predictions	Type the maximum number of recommendations to be presented on your site (used to compute the Recommendation Score). The default is 10 attributes.
   Input attribute fraction	Type the fraction of attributes to be used as input to the predictions as a number between 0.0 and 1.0. For example, specifying an input attribute fraction of 0.05 selects the most significant 5 percent of input attributes. The default value is 1.0, which includes all attributes as inputs for prediction. Values less than 1.0 are recommended if the number of attributes is very large, such as product recommendations for a catalog with over 1,000 products.
   Output attribute fraction	Type the fraction of attributes to be predicted as a number between 0.0 and 0.1. For example, specifying an output attribute fraction of 0.05 results in decision trees being built for the most significant 5 percent of attributes. (If the output attributes are products, this will return trees for the 5 percent most popular products.) The default value is 1.0, which produces trees for all attributes. Values less than 1.0 are recommended if the number of attributes is very large, such as product recommendations for a catalog with over 1,000 products.

6. Click Finish.

The status of the build process appears in the details screen.

During the build process, the status is Building. When the build process is finished, the status is Idle. If the build process is unsuccessful, a message that describes the problem appears in the details screen and is written to the Commerce Server 2002 Application Log. You can use Event Viewer to view the Application Log.

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