So I want to know whether or not my models are overfitting or the difference between train and validation errors are decent.
$L$: is the number of neighbors
The first column is the train error
The second column is the validation error
Okay. Actually I got those values from 5-fold cross validation. So the column error on the right is the averaged test error over all folds and the errors on the column on the left are the averaged train error over all folds. From what i know you are supposed to know if a model is overfitting or not by adding more data to the training set? In this example all I did was to sweep the values of the $L$ neighbors.