Indeed, but it’s important to differentiate between model execution complexity, and model optimization complexity. A linear model, if the inputs are e.g. squar

Indeed, but it’s important to differentiate between model execution complexity, and model optimization complexity. A linear model, if the inputs are e.g. squares and variable products, is a quadratic equivalent. A logistic regression yields a linear model; you could tell people it’s linear regression and they’ll likely believe you, but won’t be able to replicate. There’s a huge issue with itrelevant inputs and how to identify them - Emanuel Candes has done a lot of work on that, as did Rob Tibshirani. Saying “linear models” is saying little more than “using math”, even if that’s true, and even saying “linear regression” doesn’t give much information about what is actually being done. The bottom line is that the decision boundaries are usually simple and often have linear form - but the variables in that linear form are not raw data, but rather nonlinear transformations of it (e.g. order imbalance)