Data Mining Using SAS Enterprise Miner: A Case Study by SAS Institute

By SAS Institute

Information mining permits you to detect precious hidden details on your information and use it to unravel your enterprise difficulties. This introductory consultant to facts mining makes use of a case examine procedure that takes you thru the SAS firm Miner interface from preliminary information entry to numerous accomplished analyses, akin to predictive modeling, clustering research, organization research, and hyperlink research. when you are a brand new SAS company Miner person, you can find this pleasant advisor to be a useful source as you navigate the interface. After finishing the case reviews during this advisor, you may be ready to take on the extra advanced statistical analyses which are lined within the SAS company Miner on-line reference documentation.

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3 None — (default) all candidate effects are included in the final model. Select Stepwise by using the next to the Method box. Inspect the Effect Hierarchy options in the lower-left corner of the General subtab of the Selection Method tab. Model hierarchy refers to the requirement that for any effect in the model, all effects that it contains must also be in the model. For example, in order for the interaction A*B to be in the model, the main effects A and B must also be in the model. The Effect Hierarchy options enable you to control how a set of effects is entered into or removed from the model during the effect-selection process.

3 Median — the 50th percentile. 3 Midrange — the maximum plus the minimum divided by two. 3 Distribution-based — Replacement values are calculated based on the random percentiles of the variable’s distribution. 3 Tree imputation — Replacement values are estimated by using a decision tree that uses the remaining input and rejected variables that have a status of use in the Tree Imputation tab. 3 Tree imputation with surrogates — the same as Tree imputation, but this method uses surrogate variables for splitting whenever a split variable has a missing values.

Changing these values might affect the final variables that are included in the model. 025. Close the Regression node and save the changes when you are prompted. Since you have changed the default settings for the node, you will be prompted to change the default model name. Type StepReg for the model name. Select OK . Evaluating the Model Right-click the Assessment node and select Run. This enables you to generate and compare lift charts for the two regression models. Observe that each node becomes green as it runs.

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