When teaching ML I talk about adding loops to the Ptolemaic planets-orbit-the-Earth-model - and how it actually worked pretty well because they spent a lot of time adding loops to the orbits to make it fit. It was overfit.
When teaching ML I talk about adding loops to the Ptolemaic planets-orbit-the-Earth-model - and how it actually worked pretty well because they spent a lot of time adding loops to the orbits to make it fit. It was overfit.
> Overfitting is an undesirable machine learning behavior that occurs when the machine learning model gives accurate predictions for training data but not for new data.
However, most of the particles introduced in physics have been done with concrete predictions. For instance, when the top/bottom quarks were first proposed (1973-1975) the theoretical paper also included a bunch of predictions. In the following years all of the predictions turns out to be true.
Trust me, everyone wishes the Standard Model was simpler and was more "fundamental", but no one has figured out how to make it work. I'm not quite sure how "overfitting" fits here.