About Honest_Abe

Honest_Abe · ‎10-17-2016

Yes, for the Decision Tree node, Assessment Measure is set to its default "Decision." Other settings are as the book indicates. The same is true for Regression. Profit is mentioned in the output at each of the model nodes and the Model Comparison node: Fit Statistics Model Selection based on Valid: Average Profit for TARGET_B (_VAPROF_) Valid: Train: Valid: Average Average Train: Average Valid: Selected Model Model Profit for Squared Misclassification Squared Misclassification Model Node Description TARGET_B Error Rate Error Rate Y Tree Decision Tree 3.24914 0.18752 0.25005 0.18747 0.24994 Reg Regression 3.24914 0.18752 0.25005 0.18747 0.24994

Honest_Abe · ‎10-16-2016

I followed all of the instructions except for adjusting the prior probabilities. So I did put the weights of $14.50 and -$0.50 in. dtk's answer would make sense if all observations were placed in the positive group, but all observations are placed in the negative group when the prior probabilities are 0.25/0.75. With both 0.25/0/75 and 0.05/0.95 priors, all observations are placed in the negative group according to the validation classification matrix. But the ROC curve is y=x for 0.25/0.75 but is more concave for 0.05/0.95. My initial post should have read, "It seems that the models place every observation in the class with the larger *prior* probability."

Honest_Abe · ‎09-05-2016

I'm following the Getting Started with SAS Enterprise Miner example: https://support.sas.com/documentation/onlinedoc/miner/. If I do not adjust the prior probabilities to 0.05/0.95 as suggested, but use 0.25/0.75 instead, the regression and tree models produce models with ROC curves that appear to be y=x. In other words, they are like flipping a coin. It seems that the models place every observation in the class with the larger posterior probability. It would seem to me that adjusting the prior probabilities to 0.05/0.95 would make things worse. The help states, "Increasing the prior probability of a class increases the posterior probability, moving the classification boundary so that more cases are classified into the class." However, when you do that, the decision tree has splits and both the ROC curves are concave(ish). Why do the models produced with 0.05/0.95 priors produce "better" results than the models produced with 0.25/0.75 priors?

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Re: SAS EM prior probabilities

Re: SAS EM prior probabilities

SAS EM prior probabilities

Re: SAS EM prior probabilities

Re: SAS EM prior probabilities

SAS EM prior probabilities