Model validation performance

Muhammed_Fatih_

Hello together,

which validation performance (with regard to learning and testing phase) of classification models is quicker? Cross-calidation or the classical split validation (with a 70:30 split)?

Thank you in advance for your help!

Best regard,

Fatih

varunm1

Split validation is quicker, it builds model only once and then tests on the dataset. Incase of cross-validation, the model is built k+1 times (K is the number of folds).

I didn't encounter any special case where cross-validation performed faster than split. I don't think it happens if all other settings are the same (Feature selection, hyperparameters, etc).

Maybe if you use a processor with multiple cores and each cross-validation process is run parallelly, then there might be a chance based on the fold sizes. But in general, the above is fine.

Hope this helps.

MartinLiebig

Hi,

Cross-validation is the more accurate estimator of the true model performance.

Best,

Martin

Muhammed_Fatih_

Hi varunm,

thank you four your answer! An additional question - is there a possibility to say that one of the two validation processes (Split Validation vs. Cross-Validation) performs better in general with regard to learning and testing?

Best regards!