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positive class in logistic operator? Disagrees with AUC
Maven
First off, I want to thank everyone here, ESPECIALLY SIMON, for all the great help with my last question. I couldn't have gotten this far without you guys!!
My next question should be much simpler...
I am following a suggestion read in a paper to use a "conditional logit" function for some training. This leads to two questions:
1) Is there a "conditional logit" function in RM, or is this the same as the W-Logistic operator??
2) I learned in the past how to indicate which class is positive and which is negative in RM. There seems to be a discrepency with how RM handles it. In my current process, I indicate the "not sick" class as negative by making sure it is the first label in the arff file. The problem is that the performance classifier and the w-logistic operator seem to disagree on this:
a) The AUC for the binomial performance operator correctly states "sick" as the positive class and draws a nice curve for it.
b) The resulting model for the W-logistic operator indicates the "not-sick" class as the trained for class.
This seems wrong to me. Shouldn't the two operators agree on what is the positive class??
Thanks!
My next question should be much simpler...
I am following a suggestion read in a paper to use a "conditional logit" function for some training. This leads to two questions:
1) Is there a "conditional logit" function in RM, or is this the same as the W-Logistic operator??
2) I learned in the past how to indicate which class is positive and which is negative in RM. There seems to be a discrepency with how RM handles it. In my current process, I indicate the "not sick" class as negative by making sure it is the first label in the arff file. The problem is that the performance classifier and the w-logistic operator seem to disagree on this:
a) The AUC for the binomial performance operator correctly states "sick" as the positive class and draws a nice curve for it.
b) The resulting model for the W-logistic operator indicates the "not-sick" class as the trained for class.
This seems wrong to me. Shouldn't the two operators agree on what is the positive class??
Thanks!
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Answers
probably WEKA does interpret the values different. I don't think we can do much about it, but you could replace the WEKA learner with the RapidMiner Logistic Regression, which should be the same for binominal values.
Greetings,
Sebastian