Earlier quoted context omitted.
Serious question: I fully understand the idea of historical algorithms being no true indicator of the future. With that said....... If an algorithm consistently performs over 20+ years of data (through multiple black swan events, multiple major events), then why is it not safe to assume it likely will continue going forward? Wouldn't 20 years of "evidence" be a huge amount, such that future events likely wouldn't dev…
Overfitting, even on a 20 year dataset.
My ML knowledge is somewhat rusty... does overfitting occur more often on models with many input parameters (ie.. neural networks).
His algorithm seems very simple, without really using ML at all, it's more of just a procedural 1-2-3 step thing, with no actual learning.
Can you explain how overfitting works into his algorithm?