[autokad]

> "Can you please elaborate more on what kind of critical mistakes a machine can make, while someone with math background would not make. I am building a competing tool"

the short answer is, go study stats and fundamentals of ML instead of asking hn to build your product for you.

> "why would you trust a model which was created manually and not a model which was auto created."

one of many reasons: domain knowledge is important, and math alone cant tell you things are muffed up. contrived example: you build a linear regression model to predict home price and square footage has a negative coefficient. Math conclusion: bigger house = lower price. domain knowledge: oh, we are missing a feature and the model cant tell the difference between city homes vs rural.

there is value to auto ml but there is a lot of room to go horribly wrong

[streetcat1]

Again, my point is that for a given data set, an auto ml system is much more efficient and radically cheaper than human modeler.

You are pointing to an area outside the realm of automl (feature engineering/generation) , which is domain specific. But this was not my original question.

[autokad]

this has nothing to do with feature engineering and generation. I never added or changed any features in the example. It is exactly in the realm of automl, you run a model, -because- you are missing data, your model is making wrong assumptions.

You could argue (which you didn't) that this would fall under model interpretation, but a model in this example would probably fail to generalize and make bad predictions in the future: IE slamming home values because they have large square footage.