"Different results for X-Validation (libSVM) in version 4.6
Dear community,
I am upgrading from rapidminer version 4.6 to 5 and I'm having some difficulties that I hope maybe someone can help me with.
I am using a data set consisting of 40 example set rows with 73 attributes (72 numerical + 1 numerical label). If anyone wants to reproduce the steps, here is the data in Excel format: http://jump.fm/PFMGS.
In rapidminer 4.6 I start the wizard, open x-validation with svm, import my data, and start the process. The result is 100% accuracy. Here are some screenshots: http://img696.imageshack.us/img696/2939/rapidminer4results.png
I tried to reconstruct this in rapidminer 5:
- I imported the data into my repository and created a new process
- Since the imported data was marked nominal by rm, I use Nominal to Numerical converter for the complete dataset
- the output goes into X-Validation module (default parameters as in rm 4.6). from there ave-output goes to results
- in the Validation module it looks like this
-- in training module there is the libSVM module (C-SVC, rbf kernel, gamma=0, C=32, epsilon = 0.0010, same as in rm 4.6)
-- in testing module I use Apply Model and then Performance Module (same default values as in rm 4.6
executing the process results in 90% accuracy. Screenshots: http://img42.imageshack.us/img42/9720/rapidminer5results.png
Did I make a mistake? Thanks for your help.
Alex
I am upgrading from rapidminer version 4.6 to 5 and I'm having some difficulties that I hope maybe someone can help me with.
I am using a data set consisting of 40 example set rows with 73 attributes (72 numerical + 1 numerical label). If anyone wants to reproduce the steps, here is the data in Excel format: http://jump.fm/PFMGS.
In rapidminer 4.6 I start the wizard, open x-validation with svm, import my data, and start the process. The result is 100% accuracy. Here are some screenshots: http://img696.imageshack.us/img696/2939/rapidminer4results.png
I tried to reconstruct this in rapidminer 5:
- I imported the data into my repository and created a new process
- Since the imported data was marked nominal by rm, I use Nominal to Numerical converter for the complete dataset
- the output goes into X-Validation module (default parameters as in rm 4.6). from there ave-output goes to results
- in the Validation module it looks like this
-- in training module there is the libSVM module (C-SVC, rbf kernel, gamma=0, C=32, epsilon = 0.0010, same as in rm 4.6)
-- in testing module I use Apply Model and then Performance Module (same default values as in rm 4.6
executing the process results in 90% accuracy. Screenshots: http://img42.imageshack.us/img42/9720/rapidminer5results.png
Did I make a mistake? Thanks for your help.
Alex