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doc_plasmon
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**2**Contributor I
I'm using the LibSVMlearner process for classification (C-SVM), and I'd like to make use of the likelihoods determined via Platt scaling. I assume these are output via the "calculate-confidences" option. I'd like to calculate the mean squared error between these likelihoods and the target. I'd then like to use these as a performance metric, perhaps via the Data2Performance process? Can someone give advice on 1) how to calculate the MSE when the confidences are calculated, and 2) how to use the result in Data2Performance?

Many thanks.

Many thanks.

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## Answers

6Contributor IIThis is different from the operator BinominalClassiﬁcationPerformance, which will only evaluate 0/1 predictions.

0Newbieon the output of the FastLargeMargin SVM, then measure the MSE between the true label and the probability of that label. Maybe I need to make sure that previous operators are providing real valued predictions?

6Contributor IIFor MSE my impression is that real-valued predictions are used also. If they aren't, then MSE doesn't make much sense.

Note that if you use the Logistic Regression option of FastLargeMargin, then Platt scaling is not necessary.

2Contributor I