> "the models are 100% explainable"

In my experience this is largely illusory. People think they understand what the model is saying but forget that everything is based on assuming the model is a correct description of reality.

Your typical case of linear regression isn't even close to a correct description of reality, what gets included is largely arbitrary and due to convenience. As a result, different people with different types of data can get very different estimates for any features common to both models.

Also, stuff like this:

"I don't even think simple linear models are actually explainable. They just seem to be. Eg, try this in R:

  set.seed(12345)
  treatment = c(rep(1, 4), rep(0, 4))
  gender1   = rep(c(1, 0), 4)
  gender2   = rep(c(0, 1), 4)
  result    = rnorm(8)

  summary(lm(result ~ treatment*gender1))
  summary(lm(result ~ treatment*gender2))

Your average user will think coefficient for treatment tells you something like "the effect of the treatment on the result in this population when controlling for gender". I get a treatment effect of 1.17 in the first case, but -0.38 in the second case, just by switching whether male = 0 and female = 1 or vice versa." https://news.ycombinator.com/item?id=16719754