Bad performance when loading and applying a SVM Model
I saved a SVM model as
I got a performance of about 80%-87% for testing / training accuracy respectively.
When I now load the saved model and applied the model on some test data, I get only about 57%, and the contingency table shows me that there seems to be an issue... here is the design from loading and applying the model:
and here is the performance:
ok my previous post was cut again...so here again:
when I am doing the same process, only with a k-nn model, I get also about 80% on new test data... that's why I'm asking me if this is a SVM operator related issue, or if I did something wrong with my process, or if its a saving the model issue...
the only log message that appeared was:
Aug 5, 2016 9:18:20 AM WARNING: Kernel Model: The value types between training and application differ for attribute 'ABC', training: real, application: integer
therefore, I imported the data again, and configured real for 'ABC', the message dissapeared but the result was the same unfortunately...