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The ODM ROC Curve

Data warehouse tips by Burleson Consulting

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

A similar curve to the lift chart is the ROC(short for Receiver Operating Characteristic) curve.  The ROC curve uses the same metric on the y-axis as the lift curve, versus the number of true negatives correctly classified, for different cutoff levels. 

The default cutoff level is 0.5, but we may be more interested in customers who are more likely to have an affinity card than those who do not. 

The ROCmetric gives us the opportunity to explore ?what-if? analysis.  Let?s reduce the false negative value as much as possible with the requirement that we keep the total positive number of positive predictions under 150. 

We may have a budget restraint so we can only print 150 brochures.  The false negatives in our model amount to 77 cases.   The red vertical line is set at 0.5 probability threshold. 

By moving the red vertical line to the right, we change the values in the confusion matrix

Changing the probability threshold to 0.886 reduces the false negatives from 77 to 58, and keeps the total number of positives (52 + 95 = 147) to less than 150.   Now that we have modified the ROCchart, we can use these metrics when we apply the model to our dataset. 

Applying changes to a ROCModel

To change the actual ROC model, we can follow these steps:

1.      Return to the Mining Activitydisplay for ALL_US_NB1 and click on the ROC Threshold:0.95151359 link in the Test Metrics block. 

2.      Move the vertical red line to 0.88639 or click this value under Probability Threshold, then click OK. 

3.      You?ll see that the ROCThreshold has the new value.  You do not need to re-run the test step for this new threshold to be used when you apply the model. 

Applying the ROC Model

Now we will apply the model to new data so that we can prepare our mailing list.  This is also known as ?scoring the data?.  When a model is applied to new data, the data must be prepared and transformed in exactly the same way that the original source data was prepared for the model building.  Remember that we built the na?e classification model on a subset of the MINING_DATA_BUILD_V, which was all the 'United States of America? customers.  Now let?s apply the model to all other customers.

Generalizing the Model

We want to create a new view, so in the main toolbar, click on Data, then Create View to start the Create View Builder.  Expand the data source list under your connection and double click on the MINING_DATA_BUILD_V view.  The column names will appear in the window; click on the top-most box to select all the attributes. 

Under the Create Where Clause, choose COUNTRY_NAME and doesn?t contain from the drop-down lists, and type in ?America? in the third box.  

Click the View Results tab to see what the dataset looks like, and if satisfactory, choose Create View under File.  Type in a name for the new view, such as MINING_DATA_BUILD_V_NOUS and click OK. 

To apply the model to the MINING_DATA_BUILD_V_NOUS:

1.      Launch the Activity Guide Apply wizard from the Activity menu.

2.      Choose the ALL_US_NB1 model under Classification. 

All the information about data preparation and model metadata will be passed to the apply activity from the build model. 

 

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:

http://www.rampant-books.com/book_2006_1_oracle_data_mining.htm


 

 

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