> in traditional statistics, you can understand the parameters. I also think that this is the key differentiator between ML and stats. Statistical models can

> in traditional statistics, you can understand the parameters. I also think that this is the key differentiator between ML and stats. Statistical models can be understood formally, which means that not only we know how each parameter affects the predictions, we also know what their estimation uncertainties are, under which assumptions, and how to check that these assumptions are satisfied. Usually, we value these models not only because they're predictive but also because they're interpretable. In ML there is neither the luxury nor the interest in doing this, all we want is something that predicts as well as possible. So the difference is not the model itself but what you want to get out of it.