I am new to machine learning and I have a couple of questions about a project.
So, I created a classifier using the MNIST data set for a ML project that I was working on. I augmented the data by shifting each original point 2 pixels up, down, left, and right (so I have 240,0000 new images in addition to the 60,000 original ones). I was wondering if I should bother retraining my model with a shuffled training set, since I think (please correct me if I am very wrong) that the order of the training data in a KNN classifier does not play a factor in the final model, since predictions are made on nearest neighbors and shuffling the dataset will not change these neighbors. *As a side note, I am using the classifier/model on user drawn images, and the one with the augmented data set (unsurprisingly) performs better than the model trained only with the original data.
However, I seem to be getting the same accuracy score (the exact same to the 4th decimal place) on my test set with my augmented data set (30,000 total images) as I did with my original data set (60,000 total images). Is this something to be concerned about/did I do something wrong?
Thanks so much for your help!