"svm output range very differnt from neural net"
bp1977
New Altair Community Member
Hi,
Firstly let me thank you for a wonderful product, ive being playing with for a few days and its a lot of fun, if a bit overwhelming to start.
As a hobby Ive been using nueral nets to predict horseracing results for a while now and wanted to give svm a try.
the svm is doing well but the output range is very strange. My target is win (1) or lose(0), so i expect to get back some number between 0 and 1.instead I get ranges between -2 and 10 and the value is inverted, so for a winner it returns ~-2 and for a loser ~10.
Any idea how I can get the svm to scale the output like a nn?
Firstly let me thank you for a wonderful product, ive being playing with for a few days and its a lot of fun, if a bit overwhelming to start.
As a hobby Ive been using nueral nets to predict horseracing results for a while now and wanted to give svm a try.
the svm is doing well but the output range is very strange. My target is win (1) or lose(0), so i expect to get back some number between 0 and 1.instead I get ranges between -2 and 10 and the value is inverted, so for a winner it returns ~-2 and for a loser ~10.
Any idea how I can get the svm to scale the output like a nn?
0
Answers
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Hi,
actually it seems to me that you are trying to solve a classification problem (win or loose) by a regression. RapidMiner does all the handling of ranges and probabilities internally if you just set the label to a nominal value. So if you use the numerical to binominal operator to change the data accordingly, SVM as well as NeuralNet will produce a crisp classification and a confidence for each class.
Greetings,
Sebastian0
