As I didn't see one correct definition of overfitting: overfitting means that the model is too closely aligned to the test data, picked up noise and does not g

As I didn't see one correct definition of overfitting: overfitting means that the model is too closely aligned to the test data, picked up noise and does not generalize well to *new, unseen* data. think students that learn to reproduce questions and their answers for a test instead of learning concepts and to transfer knowledge to new questions that include the same concepts. while this sounds like overfitting, I'd just say it's garbage in, garbage out; wrong classification. the training data is shit and didn't have (enough) correct examples to learn from.