Right. I just took issue with the “unsaid” part because it makes it sound like the book makes statements that are untrue, when in fact it can at worst make statements that aren’t meaningful (“if this unrealistic assumption holds, then stuff follows”). You can call it pointless, but not silent, because well it’s not.
I’m of course completely unqualified to judge how realistic the i.d.d. assumption is, having never used ML in practice. I edited the paragraph you quoted to add a disclaimer that it is only true if the i.d.d assumption holds.
But I’d point out that this is a text book, so even if correlations are as problematic as you say, it is still a reasonable choice to present the idealized model first and then later discuss ways to model correlations in the data. No idea if this actually happens at some point.
Right. I just took issue with the “unsaid” part because it makes it sound like the book makes statements that are untrue, when in fact it can at worst make statements that aren’t meaningful (“if this unrealistic assumption holds, then stuff follows”). You can call it pointless, but not silent, because well it’s not.
I’m of course completely unqualified to judge how realistic the i.d.d. assumption is, having never used ML in practice. I edited the paragraph you quoted to add a disclaimer that it is only true if the i.d.d assumption holds.
But I’d point out that this is a text book, so even if correlations are as problematic as you say, it is still a reasonable choice to present the idealized model first and then later discuss ways to model correlations in the data. No idea if this actually happens at some point.