How to Interpret the Results from a Neural Network Model in SSAS?

I still use the same project exercises from the TK 70-448 book as the previous post. In the project, 12 variables are used to find the salient predictors for the predicable variable - Bikebuyer (yes or no).

CustomerKey is the key column.

BikeBuyer is the predictable column, and an input as well. 

The other 11 input columns are Age, CommuteDistance, EnglishEducation, EnglishOccupation, Gender, HouseOwnerFlag, MaritalStatus, NumberCarsOwned, NumberChildrenAtHome, Region, TotalChildren, and YearlyIncome.

Now let us look at the results from the Neural Network Model.

There is only one viewer for the neural network model: Attribute Discrimination. But we can see the result from two different perspectives: the entire attribute sets or a specific attribute/value.

Attribute Discrimination for All Attributes – SSAS calculates the score for each attribute on the two competing values of the predicable variables to determine which value wins the attribute. It further arranges the attributes in the descending order, as in other discrimination charts.

In the chart above, we use all of the 12 predictor variables as the input, the two competing values on the predicable value are 1 and 0. The attribute ‘Age >=71’ has the largest difference on the two values of the dependent variable, and favors the non-buyer value. This difference score is standardized as 100 as explained in an earlier discrimination chart. The attribute with the 2nd largest standardized score of 88.92 is Children at Home =3, also favoring the non-buyer. The 3rd one is Occupation = Manual, etc. The implication is simple: if a customer is older than 70, with 3 children at home, with a manual occupation, or with 5 total children, he/she tends to NOT buy a bike. On the other hand, customers with a management occupation, from the Pacific area, or with a yearly income more than $124,634, they tend to buy a bike, although the probabilities of purchase in these groups are smaller than those in the above-mentioned tend-not-to-buy groups. The other attributes in chart can be interpreted in a similar way.

Attribute Discrimination for A Specific Attribute – The drop-down box show 12 input variables. 

Let’s say, we select gender, then we click the value drop-down box on the right. It shows the corresponding possible values for gender: M, F, and Missing (we do not have missing values in the data set). Let’s say we choose M. 

The diagram will show us the attribute discrimination distribution for the male customers only. The calculation of the attribute scores and the interpretation of these scores is the same as that in the chart above for the entire attribute set. Interestingly, the conclusions from this chart are very similar to those in the entire population. In other words, gender is not a good differentiator.