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"Decision Tree With Missing Values --
I am running into some strange results. It looks like the decision tree algorithm works quite nicely with missing attributes, however when I try to apply the model on the training data set the results are much worse. Is the apply model operator capable of classifying samples with missing values, or is the decision tree algorithm just unrealisticly optimistic with missing values.
Anyone apply decision tree with missing values? ???