I am writing a paper which utilizes machine learning to guide clinical decisions regarding a certain disease. This paper will be submitted to some neonatology journals in the next couple months, but I have never written anything about machine learning in this context before. Additionally, if published this would be my first paper ever. Unfortunately, I don't have anyone of whom I can ask kinds of questions.
Currently, the technical portion of the paper includes everything we tried to select a good machine learning model. For example, all hyperparameters are listed, all attempted feature engineering is listed, all algorithms implemented are listed, etc.
A few questions:
- Should I include every model we tried including those that we didn't?
- Should I include hyperparameters for gridsearch or only the best hyperparameters?
- Should I include every method of feature engineering we implemented?
- Should I include examples of the data's structure?
General advice is appreciated too.