I have 205000 negative records plus 82000 positive records. Each record contains 22 features. I am going to train a SVM classifier.
A stratified k-fold partition with k = 10 is generally a good starting point. This is well explained on Wikipedia:
http://en.wikipedia.org/wiki/Cross-validation_(statistics)#K-fold_cross-validation
"Stratified" means that you should keep the original balance between positive record and negative records on every fold.
Dear Simone,
Actually, I am looking for a more intelligent subsample algorithm which is able to select representative samples and ignore outliers.
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