Earlier quoted context omitted.
Saying that linear regression is easier to do properly than more complex methods like random forests, DL, boosting etc is like saying that people should code assembly instead of python
This is a false dichotomy. Both OLS regression and, say, random decision forest regression have the same objective (predict values) and achieve it with similar means (build a generative model / function). They solve the same problem. Contrastingly, assembler and python are broadly aimed at completely different use cases. Broadly, whether you should move from OLS to random forest regression = SNR increase / increase i…
The GP compares python-vs-assembler and random forests-vs-linear-regression but the analogy breaks because python produces assembler and increases the programmer's general certainty concerning what they are doing. Random forests don't make their user more certain of the results as an application. Basically, Python is a relatively "unleaky" abstraction whereas complex AI algorithms a very "leaky" abstractions.