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What is the difference between cross-validation and testing?
Validation set is different from test set. Validation set actually can be regarded as a part of training set, because it is used to build your model, neural networks or others. It is usually used for parameter selection and to avoild overfitting. Test set is used for performance evaluation.
Why is validation accuracy higher than testing accuracy?
The validation accuracy is greater than training accuracy. This means that the model has generalized fine. If you don’t split your training data properly, your results can result in confusion. so you either have to reevaluate your data splitting method by adding more data, or changing your performance metric.
What’s the difference between validation and test accuracy?
No. It is a [estimate of] test accuracy. The difference between validation and test sets (and their corresponding accuracies) is that validation set is used to build/select a better model, meaning it affects the final model.
Which is more representative test or cross validation?
The test result is more representative of the generalization ability of the model because it has never been used during the training process. However the cross-validation result is more representative because it represents the performance of the system on the 80% of the data instead of just the 20% of the training set.
What’s the difference between validation and test sets?
The difference between validation and test sets (and their corresponding accuracies) is that validation set is used to build/select a better model, meaning it affects the final model.
Can a cross validation error use unseen data?
Yes, the cross-validation error uses unseen (“out-of-bag”) data. However, note that you are using the CV error in fitting your model and tuning (hyper-)parameters. And then the final model you are working with has seen these “unseen” data. Cross-validation is part of model training.