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# Calculation

Hi,

I am a student and try to understand the RapidMiner output after applying a trained model (e.g. multiclass with SVM, KNN, NB). I am getting the prediction and for each possible class/label the confidence value. But I don't know how to interpret these confidence values and how their calculation is defined.

Do you know, if there are definitions anywhere of the calculation and interpretation of the confidence values for each classification algorithm (e.g. SVM, KNN, NB)?

Best regards

mbuko

I am a student and try to understand the RapidMiner output after applying a trained model (e.g. multiclass with SVM, KNN, NB). I am getting the prediction and for each possible class/label the confidence value. But I don't know how to interpret these confidence values and how their calculation is defined.

Do you know, if there are definitions anywhere of the calculation and interpretation of the confidence values for each classification algorithm (e.g. SVM, KNN, NB)?

Best regards

mbuko

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## Answers

3,517RM Data Scientistsince the mentioned algorithms are standard algorithms you can search easily on the web. As a good quoteable, mathematical source i can recommend Hastie, Tibsheirani, Friedmann - The elements of statistical learning. But be aware - math dragons are around the corner.

For k-NN: It is simply the (weighted) fraction of examples belonging to the class.

NB: The result of the naive bayes formula. See: https://en.wikipedia.org/wiki/Naive_Bayes_classifier the one in RM is actually the gaussian one.

For SVM: i recommend the book above.. Quite some fun with lagrange multipliers. In the end classification is only a scalar product. i think this is simply mapped to [0,1]

~Martin

Dortmund, Germany

10Contributor IIBest regards

mBuko