ML looks (for many peole) like a way to circunvent your grumpy statiscian saying that the underlying data is worthless and/or you should focus on getting the data pipeline done properly for a logit model on your churn rate.
"Scientist free science," -- being able to optimize systems without understanding them, has been a dream of the business world since the dawn of time. There's always been a market for cookbook recipes that automate the collection of data, and interpretation of results. Before ML, there were "design of experiments," and "statistical quality control."
>Before ML, there were "design of experiments," and "statistical quality control."
Statistical quality control, at least the way I know it, is very useful in finding problems in your process. I'm also not sure how this fits with your premise. It's about optimizing systems by first finding out where to look, and then looking there in detail with expert knowledge, i.e. deep understanding of your system.
I'm definitely with you there, but I've also seen the side of it where it turns into a cargo cult and runs headlong into the replication crisis.
Perhaps the good thing is that as the new things gain popular attention, the old techniques such as SPC are under less pressure to support success theater, and revert to being actual useful, solid tools.
