I split my data into 80% training and 20% testing data set. Using the 80% split and 10 cross-validations, I build the model and get the training accuracy. Then I test my model on the 20% split and get the testing accuracy.
The question is: which is important training or testing accuracy?
If I used 10 different machine learning algorithms on the same split, which accuracy will guide me for the best algorithm, training or testing accuracy?
I searched on CV, but the following similar questions are unanswered: