Model Build Properties
The following table shows the options and functions available on the Model Build Properties dialog box.
| Use this | To do this |
| Name | Type a name for the analysis model. |
| Build priority | Select the build priority you want to set for this analysis model. The default is Normal (which is the build priority for the particular build thread). Selections are:
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| Model type | Select an analysis model type to build. Selections are:
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| Sample size | Type the number of cases that are to be used to build the analysis model. You can change the default value if necessary. The default is -1 (all data) or 20,000 cases 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 fraction, the model will not be scored. If you type 0.4, 40percent of the sample data will be used to score the model (the remaining 60percent is 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 0.1. For example, specifying an input attribute fraction of 0.05 selects the most significant 5percent 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 trees being built for the most significant 5percent of attributes. (If the output attributes are products, this will return trees for the 5percent 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. |