[bunderbunder]

Yeah, I'm not sure either, that seems to be an editing mangle.

The bit about "it's safer to train the model and observe its performance", though, is reasonable advice. The main problem with multicollinearity is that it gives you a model with poorly-defined coefficients - that is, their standard errors are high. That's a big problem if you're coming at the problem with a statistician's mindset and trying to come up with a parsimonious model with statistically significant parameter estimates. If you're just going for the best predictive model you can get, though, then you don't necessarily care about super tight standard errors on all your coefficients, so it's not such a big deal.