Comparing the Results

Figure 1. Fields added for Polynomial function
Fields added for Polynomial function
  1. Scroll the table output to the right to see the newly added fields.

    The generated fields for the Polynomial function type are named $S1-Class and $SP1-Class.

    The results for Polynomial look much better. Many of the propensity scores are 0.995 or better, which is very encouraging.

  2. To confirm the improvement in the model, attach an Analysis node to the class-poly model nugget.

Open the Analysis node and click Run.

Figure 2. Analysis node
Analysis node

This technique with the Analysis node enables you to compare two or more model nuggets of the same type. The output from the Analysis node shows that the RBF function correctly predicts 97.85% of the cases, which is still quite good. However, the output shows that the Polynomial function has correctly predicted the diagnosis in every single case. In practice you are unlikely to see 100% accuracy, but you can use the Analysis node to help determine whether the model is acceptably accurate for your particular application.

In fact, neither of the other function types (Sigmoid and Linear) performs as well as Polynomial on this particular dataset. However, with a different dataset, the results could easily be different, so it's always worth trying the full range of options.

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