Noah Silverman wrote:
Hello,
I'm using an SVM for predicting a model, but I'm most interested in the
probability output. This is easy enough to calculate.
My challenge is how to measure the relative performance of the SVM for
different settings/parameters/etc.
An AUC curve comes to mind, but I'm NOT interested in predicting true vs
false. I am interested in finding the most accurate probability
predictions possible.
I've seen some literature where the probability range is cut into
segments and then the predicted probability is compared to the actual.
This looks nice, but I need a more tangible numeric measure. One
thought was a measure of "probability accuracy" for each range, but how
to calculate this.
Any thoughts?
-N
Noah,
This is a big area but I'm glad you are interested in probability
accuracy rather than the more frequently (mis)-used classification
accuracy. There are many measures available. For independent test
samples the val.prob function in the Design package provides many.
When making a calibration plot to demonstrate absolute prediction
accuracy, it is not a good idea to bin the predicted probabilities.
val.prob uses loess to produce a smooth calibration curve.
Frank
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Frank E Harrell Jr Professor and Chair School of Medicine
Department of Biostatistics Vanderbilt University
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