Currently, I started to study machine learning to write an academic paper.
Lets say I have 1000 data, and I split to 70:30 for training:testing.
While training the machine learning (assume binary classification model is KNN), I added the 10-fold cross validation to validate the training accuracy as well as to avoid the over-fitting problem.
With this methods, I found the optimized hyper-parameter for machine learning classifier which shows the highest validation accuracy.
And then, I utilized the test sets, which was not used in training, to get testing accuracy.
My question is....
Is it okay to include the prediction data from cross-validation for overall accuracy?
in training, Cross-validation:
Tr_label1: true, Tr_prediction1:false
Tr_label2: true, Tr_prediction2:true
Training validation accuracy: 50%
Tst_label1: true, Tst_prediction1:true Tst_label2: true, Tst_prediction2:true
test accuracy: 100%
over all accuracy: