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Adding a Data Source to the Model

Data warehouse tips by Burleson Consulting

This is an excerpt from Dr. Ham's premier book "Oracle Data Mining: Mining Gold from your Warehouse".

Next, we select the apply data source that we created as MINING_DATA_BUILD_V_NOUS in Step 2 of the Apply Data wizard, and click Next. 

Now you have the option of selecting additional columns to be included in the table resulting from the Apply operation.  The wizard suggests that you include the customer identity attribute so that you can see which customers are most likely to have an affinity card. 

By default, the ?Apply Result? contains only the case identifier and prediction information.  You may want to keep the ?bare bones? set of predictor variables and join this with tables containing the customer contact information later.  Select some or all of the attributes in Step 3, and click Next. 

The next step allows you to choose the format for the output table.  When the model is applied to a particular customer, a score is generated for each possible target value.  A sorted list is generated from the most to least likely value.  This list will have only two entries since our target is a binary, because our customers either have or do not have an affinity card. 

Viewing Top Rankings

If your target had multiple results, as in the case of predicting which of 10 stores a customer was most like to shop, then you might want to see the ranking of top three choices for each customer, and would click the radio button next to ?Number of Best Target Values? and enter in 3.  The output table would have three rows for each individual containing the prediction information for the top three stores.

In the case of which of ten stores the customer is most likely to shop, you may only be interested in a customer?s ranking probability for a particular store.  Then you would check the radio button next to ?Specific Target Values? and check the box next to the particular store you were interested in.  The result would be a table with one row for each customer with the prediction for that store, even though the probability might be extremely low. 

Using the Classification Apply Option

The Classification Apply Optionhas as the default the most probable target value or lowest cost.  In our case, we?ll keep the default selection and click Next, opting for an output table with one row per customer.  Choose a name for the Apply Data Mining Activity, such as ALL_US_APPLY_NON_US.

When the Activity has finished running, click Result in the Apply section to view the output table.  Since we chose the ?Most Probable? classification option, the table contains the customer or case ID, then the predictor variables (prediction, probability, cost and rank), then the additional attributes you chose to have in the output table. 

The prediction is the most likely target value, and the probability is the confidence in that prediction.  Cost represents the cost of a wrong prediction, with low cost meaning high probability.  The rank will be one for all cases since we did not choose a predictor variable with more than 2 possible outcomes, and consequently do not have more values to predict. 

We have now completed building, testing and applying a model to our customer data.  We can go back to the boss having produced a mailing list in our Oracle data warehouse that should draw in the most promising customers.   

For more tips and tricks for Oracle data warehouse analysis, see Dr. Ham's premier book "Oracle Data Mining: Mining Gold from your Warehouse"

You can buy it direct from the publisher for 30%-off:



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