Maybe I'm misunderstanding this but this doesn't seem like an accurate explanation of overfitting: > In machine learning (ML), overfitting is a pervasive pheno

Maybe I'm misunderstanding this but this doesn't seem like an accurate explanation of overfitting: > In machine learning (ML), overfitting is a pervasive phenomenon. We want to train an ML model to achieve some goal. We can't directly fit the model to the goal, so we instead train the model using some proxy which is similar to the goal One of the pernicious aspects of overfitting is it occurs even if you can perfectly represent your goal via a training metric. In fact it's even worse simetimes as an incorrect training metric can indirectly help regularise the outcome.