Classification

The classification table shows the practical results of using the logistic regression model. For each case, the predicted response is Yes if that cases's model-predicted probability is greater than the cutoff value specified in the dialogs (in this case, the default of 0.5).

• Cells on the diagonal are correct predictions.
• Cells off the diagonal are incorrect predictions.

Of the cases used to create the model, 57 of the 124 people who previously defaulted are classified correctly. 352 of the 375 nondefaulters are classified correctly. Overall, 82.0% of the cases are classified correctly. From step to step, the improvement in classification indicates how well your model performs. A better model should correctly identify a higher percentage of the cases.

Classifications based upon the cases used to create the model tend to be too "optimistic" in the sense that their classification rate is inflated. Subset validation is obtained by classifying past customers who were not used to create the model. These results are shown in the Unselected Cases section of the table. 80.6 percent of these cases were correctly classified by the model. This suggests that, overall, your model is in fact correct about four out of five times.

Even the subset validation is limited, however, because it is based on only one cutoff value. It is more useful to save the predicted probabilities, then construct an ROC Curve. See the topic ROC Curve for more information.

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