Examining the Data
On the Model tab, the Predictor Importance graph shows the relative effect of the various fields on the prediction. This shows us that BareNuc has easily the greatest effect, while UnifShape and Clump are also quite significant.
- Click OK.
- Attach a Table node to the class-rbf model nugget.
- Open the Table node and click Run.
Figure 2. Fields added for prediction and confidence value 
- The model has created two extra fields. Scroll the table output to the right to see them:
| New field name | Description |
|---|---|
| $S-Class | Value for Class predicted by the model. |
| $SP-Class | Propensity score for this prediction (the likelihood of this prediction being true, a value from 0.0 to 1.0). |
Just by looking at the table, we can see that the propensity scores (in the $SP-Class column) for most of the records are reasonably high.
However, there are some significant exceptions; for example, the record for patient 1041801 at line 13, where the value of 0.514 is unacceptably low. Also, comparing Class with $S-Class, it's clear that this model has made a number of incorrect predictions, even where the propensity score was relatively high (for example, lines 2 and 4).
Let's see if we can do better by choosing a different function type.