If you had a classification task and were not allowed to tune hyperparameters, which model would you use?

If you think logistic regression is machine learning, you are right.

If you think logistic regression is statistics, you are right.

If you think it can only be either or, you are wrong.

What would a minimal, SOTA supervised ML course look like?

- LLMs
- xgboost
- CNNs
- CV
- metrics
- random search (tuning)
- one-hot encoding
- Linear/logistic regr.

What else?