Benjamin-Lee
There's more than one way to do it: find the learning that's right for you (and your data)
Have you checked the list of proposed rules to see if the rule has already been proposed?
- [X] Yes
Feel free to elaborate, rant, and/or ramble.
Any citations for the rule? (peer-reviewed literature preferred but not required)
- DOI
Could possibly be linked to a figure where data (or multiple examples) through different classifiers (and different parameters) gives different precision/recall.
I think a visual like this might be very impactful especially for someone just starting out.
Similarly, some text on the importance of parameters into your models (eg alpha, number of layers, etc etc), what they mean, and how they can influence how you might interpret results
A nice corollary to this might be to favor convolutional networks over recurrent networks when determining model feasibility for sequential data (e.g. DNA sequence). Many features of CNNs, such as their gradient stability, can make them more amicable in the earlier stages of a project. This paper is a good source on the topic.
