Because of the differences in representation for classification models, different attributes may be judged more important for different models. A sample file, provided with MineSet, is discussed at the end of this chapter. It determines how important various columns are in discriminating the different values of the label column you choose. You might, for example, want to find the best three columns for discriminating the label good credit risk so you can choose them for mapping to axes the Scatter Visualizer. Adding more columns can only increase the purity.
Purity is a measure of the skewness of the label value distribution.
The cumulative purity measure is a measure of the purity of partioning the data. The data is partioned using columns found as importand in the same way data is partitioned in a Decision Tree. Each set in the partition has its own purity measure, and the purity measure within the partion is a combination of these individual measures. For a given set in the partition, the purity is 0 if each class has equal representation, and 100 if every record is of the same class. Similarly, the cumulative purity will be 0 if each set in the partition has an equal representation of classes, and 100 if each set in the partition contains record that all have the same class.