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[Solved] Data splitting for SVM parameters selection vs Neural Networks
Contributor II
Hi,
I have a question about SVM parameter selection and data spliting. I need to know that is it sufficient and efficient to split the whole data set into just 2 part (train set and test set) then use cross validation on train set and select the C and gamma which to lead the best performance. Some people split data to 3 sets( train, cross-validation, test) for neural networks and select the parameters when the performance of cross-validation start to reduce and train performance increase.Is split the data set into just 2 parts and cross validate on train set good enough and acceptable for modeling with support vector machine or the same procedure(split into 3 set) should be done for SVM? Is splitting into 2 part procedure applicable to Neural Network?
Thanks.
I have a question about SVM parameter selection and data spliting. I need to know that is it sufficient and efficient to split the whole data set into just 2 part (train set and test set) then use cross validation on train set and select the C and gamma which to lead the best performance. Some people split data to 3 sets( train, cross-validation, test) for neural networks and select the parameters when the performance of cross-validation start to reduce and train performance increase.Is split the data set into just 2 parts and cross validate on train set good enough and acceptable for modeling with support vector machine or the same procedure(split into 3 set) should be done for SVM? Is splitting into 2 part procedure applicable to Neural Network?
Thanks.
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Answers
since the cross validation already performs the split into training and test set it should be sufficient to use *only* the cross validation and not split the data manual at all. If you are unsure, just increase the number of folds in the X-Validation.
Best regards,
Marius