Building a New Analysis Model
You can build multiple analysis models for each site. The model build process is resource intensive, so it is recommended that you build an analysis model only when the impact on your computer will be minimal.
The time required to build an analysis model depends on the size of the input data (total number of cases), the sample size, the number of attributes in the data, and the hardware and topology of your servers.
To build a new analysis model
Expand Commerce Server Manager, expand Global Resources, expand Predictor on <server name>, expand the second Predictor on <server name>, and then click Model Configurations.
In the details screen, right-click the model configuration you want to use to build an analysis model, and then click Build.
In the Model Build Properties dialog box, do the following:
Use this To do this Name Type a name for the analysis model. Build priority Select the build priority for this build. The default is Normal (which is the build priority for the particular build thread). Model type Select from the drop-down list either prediction or segment for this model. Click Next.
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 rows, you may want to specify that the Predictor resource use only 20,000 rows to build the analysis model. The default is -1 or 20,000, whichever is less. 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. Number of Segments Type the maximum number of segments in which to partition the users. This is an initial hint for the algorithm, which may find fewer significant segments than this value. This value is only available if you are building a Segment model. Buffer size Type the size of the buffer that will be used to read cases during segmentation. The default is 1,000,000 bytes. The buffer size can affect the build time and quality of the model. For example, if the model contains many attributes, you should set a large buffer size. The system resources of the computer running the Predictor resource determine buffer size limitations. This value is only available if you are building a Segment model. Click Finish.
The status of the build process is displayed 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 is displayed and then written to the Commerce Server 2000 Application Log. You can use Event Viewer to view the Application Log.
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Note
- If you build your own Prediction model configuration, you can explicitly list the attributes you want to use for input and output. When you do, the attribute fractions shown in Step 6 apply to these lists. For example, if you put five attributes in the input list and you specify an input attribute fraction of 0.8, then only the four most informative features from that list are used as input attributes when the analysis model is building.