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# Classification by Regression Operator vs. Polynomial By Binomial Classification

Hi,

I have a multi class approach by using SVM (mySVM) and a operator to enable the multi classification. The problem is, that the two possible operators "Classification by Regression Operator" and "Polynomial By Binomial Classification" lead to different kind of results for the confidence values:

1) Classification by Regression Operator: confidence value element of (-β, 1]. This seems to be the signed distance to the hyperplane. Is this correct? Why are there no values higher than 1? (1 would mean that it is on the edge of the margin. Might it depend on the kernel function?)

2) Polynomial By Binomial Classification: confidence value element of [0,1]. Is this any kind of probability? Definition?

Unfortunately, I don't find any hint how the confidence values are defined (with regard to the used operators or SVM implementation).

In order to use RapidMiner and the output, I need a clear understanding of the parameters, confidence values and dependencies to the RapidMiner operators.

I would be pleased, if you would help me with these issues!

Best regards,

Mark

PS: I have already opened a similar thread in another category (https://rapid-i.com/rapidforum/index.php/topic,9418.msg31536.html)

I have a multi class approach by using SVM (mySVM) and a operator to enable the multi classification. The problem is, that the two possible operators "Classification by Regression Operator" and "Polynomial By Binomial Classification" lead to different kind of results for the confidence values:

1) Classification by Regression Operator: confidence value element of (-β, 1]. This seems to be the signed distance to the hyperplane. Is this correct? Why are there no values higher than 1? (1 would mean that it is on the edge of the margin. Might it depend on the kernel function?)

2) Polynomial By Binomial Classification: confidence value element of [0,1]. Is this any kind of probability? Definition?

Unfortunately, I don't find any hint how the confidence values are defined (with regard to the used operators or SVM implementation).

In order to use RapidMiner and the output, I need a clear understanding of the parameters, confidence values and dependencies to the RapidMiner operators.

I would be pleased, if you would help me with these issues!

Best regards,

Mark

PS: I have already opened a similar thread in another category (https://rapid-i.com/rapidforum/index.php/topic,9418.msg31536.html)

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## Answers

2,637RM Data Scientist~martin

Dortmund, Germany

10Contributor IUnfortunately, a clear description and definition of the output and operators are missing, but I need it in order to work scientifically correct. I am looking forward to an official answer from RapidMiner, but at the moment there is an absence of any reaction.

2,637RM Data Scientisti guess there will be more offical answer than my postings, except if you ask at support.rapidminer.com, but this requieres a licence. I am employed at RapidMiner; That makes it kind of offical?

What you can do is have a look at the source. The classification by Regression operator can be found at:

https://github.com/rapidminer/rapidminer-studio/blob/master/src/main/java/com/rapidminer/operator/learner/meta/ClassificationByRegression.java

There we find the comment: I checked the attached example process. Apperently confidence(Mine) is [0,1] but confidence(Rock) not. For some reason only one confidence is set "correctly". I will check the code deeper once I have more time

~Martin

Dortmund, Germany

10Contributor IThe comment is also written in the documentation. I have the same problem with the different confidence outputs: only one is set "correctly" (but only with 1 ?).

What is the definition of the confidence value if it is in [0,1]? (I wanted to avoid looking into the implementations, but it seems to be necessary)

2,637RM Data ScientistThe value is calculated for every algorithm differently. For a k-nn it is the fraction of neighbours with this class (in the unweighted case). For the SVM the value depends on the distance to the seperating hyperplane.

Dortmund, Germany

10Contributor IWhy are the confidence values for the same SVM algorithm but different operators within a different range (which is not documented)?

In the case of libSVM there is the possibility to estimate the probability as confidence instead of the distance to the hyperplane. Unfortunately, the specific meanings of the confidence values regarding the algorithms etc. are not documented.

I think I have to look into the implementions since the academic support does not help either.

Thanks so far, I would be very pleased about any further support.