Honestly, really comes off as word salad lol.

I haven’t read the details, but it sounds like resampling in a serial learner?

Mm, not really.

Bootstrapping is used to determine the standard error of estimates using resampling. From here we can derive tools like confidence intervals, or other interval estimates.

Generally speaking you do not use the bootstrap to tweak the parameters of your model. You use cross validation to do so.

This really isn’t a surprise, where’s there’s gender equality; there tends to be more focus on human rights as a whole.

I mean, there will never be true equality when women don’t have to sign their bodies over to selective service or get a bunch of rights stripped away. Or when we overly commoditize the attention and speech (and just general financial, and social support) around women’s rights and empowerment when the education gap, sentencing gap, and mental health/suicide/substance abuse/homeless gap is continuing to widen with women being the “favored”, which harms men and drives them towards shit like trumpism.

The solution to your problem is ironically empowering men more, as the motherhood gap does have measurable affects on say; one’s earning ability.

Any problem where you want things like effect estimates lol. Or error estimates. Or models that generate joint distributions

So, literally a ton of them. Which industries don’t like things like that?

I agree with you. I was just pointing out that to say they are the only solution is foolish, as the quote implied

This quote could have just been used without much context, so grain of salt.

Linear models are often preferred for the reasons you mentioned. Under fitting is almost always preferred to overfitting.

Because they are useful for some problems and not others, like every algorithm? Nowhere in my statement did I say they are monolithic in their use across all subdomains of ml

The statement was that deep learning is the only thing that works at scale. It’s not lol. Deep learning struggles in a lot of situations.

As you mentioned, this is highly dependent on the functional relationship of the data.

You wouldn’t not use domain knowledge to determine that.

Additionally, non linear models tend to have their own drawbacks. Lack of interpretability, high variability being some of them

?

Lol. The fact that we use general linear models in every scientific field, and have been for decades should tell you all you need to know about this statement.