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Viewing Adaptive Bayes Network 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".

After completion of the Mining Activity, we click on ?Result? under ?Test Metrics?.  Since there were seven possible outcomes of the target attribute, under the Accuracy tab you can see the percentage of correctly classified cases for each value of forest cover. 

As we see, the best predictions were made for Target value = 5 (aspen), with 93% correctly classified.  The model misclassified all cases of ponderosa pine, with Target value = 3. 

The Adaptive Bayes Network model seems to be good at predicting scarcer types of forest covers. 

The default build settings are used to find a model that is good at predicting all classes by optimizing the Maximum Average Accuracy of the model, which is 60% in this example.  You can choose to build a model to maximize overall accuracy, but generally you?ll want one that attempts to classify all the classes. 

What has the model used to decide which forest cover to predict?  To view the human-readable rules, click on Result in the Build step of the Mining Activity, and look under the Rules tab. 

Here you see that the model used wilderness area and elevation to classify forest cover.  Reading over the rules, you can see common elements for the various types of trees, for example rules # 70 and # 75.  The wilderness area can be either 1 or 0, and if the elevation is = 4 then the forest cover is = 1. 

Interpreting Adaptive Bayes Network Results

The Adaptive Bayes Network model was very good at predicting forest cover = 7, and you can see from rules # 76 and #71 that this type of tree grows outside the wilderness area at elevation = 5.  To see what elevations are grouped in bin 5, we can return to the Show Summary Single-Record and examine the histogram  for Elevation.  Using Equal Width Strategy, group 5 contains elevations from 2858.5 to 3058.4 feet.

The Support (%)  for a rule is the percentage of cases in the build dataset having the predicted target value.  For rule #73, the percent confidence is high at 95%, but support is low at 2.5%, indicating that there is a marked improvement in accuracy provided by this rule, but it works for only a few cases.  When a single feature model is applied to another dataset, the output of the apply activity identifies the rule used to predict the classification result for each case. 

The rules for this dataset are quite simple, having only two attributes in the If condition, because we used a small sample (10,000 rows) from the case dataset.  In larger datasets, the rules can become very complex.  The optimal number of rows of data depends on the nature of the data, and can be as high as 100,000 records.  As a general rule of thumb, you should not expect meaningful rules unless the case data if over 20,000 rows.

Let?s say that we were really interested in classifying ponderosa pine (Target value = 3), which this first model completely misclassified.  How do we influence the model to detect this type of forest cover?  We have the choice of two different methods, Priorsand Costs, which will build bias into our 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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