This table displays the prior probabilities for membership in groups. A prior probability is an estimate of the likelihood that a case belongs to a particular group when no other information about it is available. Unless you specified otherwise, it is assumed that a case is equally likely to be a defaulter or nondefaulter. Prior probabilities are used along with the data to determine the classification functions. Adjusting the prior probabilities according to the group sizes can improve the overall classification rate.

To obtain a classification using non-uniform priors, recall the Discriminant Analysis dialog box.

Click Classify.

Select Compute from group sizes.

Select Within-groups.

Click Continue.

Click OK in the Discriminant Analysis dialog box.

The prior probabilities are now based on the sizes of the groups. A priori, 75.2% of the cases are nondefaulters, so the classification functions will now be weighted more heavily in favor of classifying cases as nondefaulters. The overall classification rate is higher for these classifications than for the ones based on equal priors. Unfortunately, this comes at the cost of misclassifying a greater percentage of defaulters. If you need to be conservative in your lending, then your goal is to identify defaulters, and you'd be better off using equal priors. If you can be more aggressive in your lending, then you can afford to use unequal priors.