"[Solved] Choosing best parameters resulted by Cross validation"
Hi all,
I need to build a model by SVM. I have used grid search and cross validation (k=2 to 20) in order to find best parameters. The problem is it that when i log cross validation accuracy, there is a lot of parameter combination which has same accuracy and same confusion matrix but when I apply those parameters on test data set i get very different accuracies (from 90 to 60). In real world problems we have no acess to test data set, So how should i select the best combination?
Thanks.
I need to build a model by SVM. I have used grid search and cross validation (k=2 to 20) in order to find best parameters. The problem is it that when i log cross validation accuracy, there is a lot of parameter combination which has same accuracy and same confusion matrix but when I apply those parameters on test data set i get very different accuracies (from 90 to 60). In real world problems we have no acess to test data set, So how should i select the best combination?
Thanks.