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Interpreting Na?e Bayes Model Results

Data warehouse tips by Burleson Consulting

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

Upon completion of the Build Activity, we can view the results. 

We can see that the elevation has the greatest influence on type of forest cover, with Soil Type 3 a distant second in importance. 

Three man-made features came in next:  roads, distance to fire points, and designated wilderness areas.  You can report these results, and use them in a Na?e Bayes analysis as shown previously.   

We will go ahead and perform the Adaptive Bayes Network analysis, which uses a built-in Attribute Importance methodology when building the model. 

Both the Adaptive Bayes Network and the Decision Tree algorithms rank attributes as part of the model building algorithm, so Attribute Importance is most useful as a preprocessor for Na?e Bayes or Support Vector Machines.

The Na?e Bayes model is something like a black box, and we cannot see what is used to create the final results.  One of the advantages to using the Adaptive Bayes Network is that you can generate human-readable rules that can give us insight as to what the model is using to classify cases. 

Using the Adaptive Bayes Network Model

Let?s start a new Classification Mining Activity and use the Adaptive Bayes Network for the activity type. 

1.     Pick COVER_TYPE_IMP as the case table and Compound or None for the Unique Identifier. 

2.     Select all the columns to be used in the analysis, skip joining other tables, select TARGET  (forest cover) as the target, and review the settings.  Make sure that the target attribute is a categorical mining type, otherwise ODMr will stop you from running the Activity.   

When you select the preferred target value, you have the choice of 1 through 7.  Pick the type of forest cover that you are most interested in to test the model.  You can change this later, so to get started choose Target - 4.  After you have named the activity, and on the Final Step page, select Advanced Settings and examine the Advanced Settings Dialog

Until this point, all steps in the Build Activity are identical to those for Na?e Bayes.  If you click on the Build tab, and then Algorithm Settings under options, you?ll see a drop down box with three selections for Model Type:  Single Feature, Multi Feature, and Na?e Bayes.  Setting the model type to Single Feature (the default) will give you the human-readable rules. 

The speed of building the model can be slower or faster depending on the number of predictors chosen for the model.  You can also limit the build time by entering the number of minutes you want the algorithm to execute.  We will keep all the defaults at this point and go ahead and finish the model building activity.

This is a large dataset; you can build the model on the entire dataset if you have enough computer resources (i.e. memory), or you may choose to build the model on a sample of the data.  To speed development of classification models, it often the case that models are built on smaller subsets of data, or limits set for the amount of time (minutes) used to build the 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:



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