I've been evaluating an EKM trainer on a set of data and my best result was (the output from cross_validate_trainer):
gamma: 0.00125, C: 78125 cross validation accuracy: 0.93135 0.193669
Trying to do the 3-fold cross-validation myself I've come up with these values:
0: 0.707792
1: 0.727715
2: 0.690438
These represent the the number of correct guesses divided by the number of samples.
What does the second number in the return value of cross_validate_trainer represent?
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Found the answer myself in svm_ex.cpp:
// The first element of the vector is the fraction of +1 training examples
// correctly classified and the second number is the fraction of -1 training
// examples correctly classified.
This seems like a good addition to the documentation.
If you would like to refer to this comment somewhere else in this project, copy and paste the following link:
I've been evaluating an EKM trainer on a set of data and my best result was (the output from cross_validate_trainer):
gamma: 0.00125, C: 78125 cross validation accuracy: 0.93135 0.193669
Trying to do the 3-fold cross-validation myself I've come up with these values:
0: 0.707792
1: 0.727715
2: 0.690438
These represent the the number of correct guesses divided by the number of samples.
What does the second number in the return value of cross_validate_trainer represent?
Found the answer myself in svm_ex.cpp:
// The first element of the vector is the fraction of +1 training examples
// correctly classified and the second number is the fraction of -1 training
// examples correctly classified.
This seems like a good addition to the documentation.