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IBM SPSS Modeler Cookbook by Scott Mutchler, Tom Khabaza, Meta S. Brown, Dean Abbott, Keith McCormick

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Correcting a confusion matrix for an imbalanced target variable by incorporating priors

Classification models generate probabilities and a classification predicted class value. When there is a significant imbalance in the proportion of True values in the target variable, the confusion matrix as seen in the Analysis node output will show that the model has all predicted class values equal to the False value, leading an analyst to conclude the model is not effective and needs to be retrained. Most often, the conventional wisdom is to use a Balance node to balance the proportion of True and False values in the target variable, thus eliminating the problem in the confusion matrix.

However, in many cases, the classifier is working fine without the Balance ...

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