Use a simple linear and non-linear model as a baseline for measuring progress.

**Have you checked the [list of proposed rules](https://github.com/Benjamin-Lee/deep-rules/issues?q=is%3Aissue+is%3Aopen+label%3Arule) to see if the rule has already been proposed?**

- [x] Yes

Use a simple, linear model, e.g., (multinomial) logistic regression or (multiple) linear regression as a performance baseline. This may be expanded to also include off-the-shelf easy-to-use ensemble methods like random forests that are relatively robust and don't require much tuning to work well out of the box.

There are many situations where a ML/DL expert can, without much doubt, say that throwing DL at the problem (given the size of the dataset and the nature of the task) doesn't make sense. However, let's face it, DL is popular, and people want and will use it even if it is not always the best thing to do in every situation. I think a good thing to recommend though is to start with a linear model as a baseline and compare the DL efforts to it.

But on the more positive side, using a simple model as a baseline is also useful in situations where using DL does make sense.

**Any citations for the rule?** (peer-reviewed literature preferred but not required)

- -

Start by reviewing the linked list of proposed rules and the eight-comment discussion, then identify the manuscript or rule entry point for this guidance. Done means an agreed recommendation covering simple linear or non-linear baselines and supported by appropriate citations; the issue does not name a file or test.

Written by the indexing model from the issue text.