Building Better Models with JMP Pro by Jim Grayson

Author:Jim Grayson
Language: eng
Format: epub

The fitted model has probabilities of Offer Accepted = Yes in the range

[0.0044, 0.6738]. Recall that when JMP classifies rows with the model, it uses a default of Prob > 0.5 to make the decision. In this case, only one of the predicted probabilities of Yes is > 0.5, and this one node has only 11

observations: 8 yes and 3 no under Response Counts in the bottom table in

Figure 6.29. The next highest predicted probability of Offer Accepted = Yes is 0.2056. As a result, all other rows are classified as Offer Accepted = No.

The ROC Curve

Two additional measures of accuracy used when building classification models are Sensitivity and (1-Specificity). Sensitivity is the true positive rate. In our example, this is the ability of our model to correctly classify Offer Accepted as Yes. The second measure, (1-Specificity), is the false positive rate. In this case, a false positive occurs when an offer was not accepted, but was classified as Yes (accepted).

Instead of using the default decision rule of Prob > 0.5, we examine the decision rule Prob > T, where we let the decision threshold T range from 0 to 1. We plot the Sensitivity (on the y-axis) versus the (1-Specificity) (on the x-

axis) for each possible threshold value. This creates a Receiver Operating Characteristic ( ROC) curve. The ROC curve can be displayed by selecting that option from the top red triangle in the Partition report.

Figure 6.30: Credit, ROC Curve for Offer Accepted

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