Data Mining In 2005 - Same Input Columns Resulting In Different Decision Trees

While recently working with several mining models, I came across something that struck me as pretty odd - and I'm hoping to find an explanation for the behavior.

Consider the following setup:


A single table in the relational database represents the only case table
A single, continuous column is the predictable
A mining structure has been created

The mining structure contains a single model, based on the MS Decision Trees algorithm
Input columns were selected for the model via the BI Studio wizard (i.e., those provided via the "Suggest" button)
The structure has been fully processed
Now, the interesting parts:


I view the scatterplot for the mining model, under the Mining Accuracy Chart tab
Back on the Mining Structure tab, I delete one of the input columns
I add the same column back into the structure
The structure is fully processed again
When I view the scatterplot for the mining model, under the Mining Accuracy Chart tab, a different set of data points are presented for the model predictions
A different set of decision trees under the Mining Model Viewer tab confirms thisHow could different patterns have been found this second time around, even though all of the input columns were the same (as well as the training cases)?

(Note: I encountered this situation while creating a new mining model that was identical to an existing one. Even though the models received the exact same inputs and training cases, they yielded different results. I was able to reproduce the behavior by using steps 1-6 above, though.)

Can someone provide some insight on this behavior, or some kind of explanation of what may be happening?


Thanks,
Joe Miller