Timeline for Random Forest Parameter Settings for Big Data
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Sep 19, 2021 at 6:28 | comment | added | Tim | @Lily it talks about machine learning algorithms in general, it applies to random forest as well. | |
| Sep 19, 2021 at 1:20 | comment | added | LCheng | Hi, I don't think the book talks about random forest interpretation though. There is only one section on decision tree. | |
| Sep 17, 2021 at 18:30 | comment | added | Tim | @Lily if you want to use a rule of thumb, just stick to the defaults the software uses, but this won't work in many cases. As for interpretability, there are whole books on this subject and we have several threads on this subject. | |
| Sep 17, 2021 at 18:14 | comment | added | LCheng | For the feature importance ranking, my goal is not doing feature selection. I want to understand them from a scientific point of view, so it’s important for me to know if a feature really contributes to explaining the label or if it just performing similarly as an arbitrary variable. Does that make sense? Any suggestion will be appreciated. | |
| Sep 17, 2021 at 18:14 | comment | added | LCheng | For the number of trees and the depth of trees, I was expecting some “rule of thumb” protocols. I’ve read many other Q&As before I raised my questions and I knew that for the number of trees, the more the better; and for the depth of trees, the shallower the more computationally inexpensive. However, that’s not really getting me anywhere in implementing my algorithms to the data. What I would like to learn more is something like what value would be good to start trying out if you were me, and when would you feel it’s good enough and you would stop trying more values. | |
| Sep 17, 2021 at 18:14 | comment | added | LCheng | Hi, thank you for the quick reply. I have a few follow-up questions. | |
| Sep 17, 2021 at 17:53 | history | answered | Tim | CC BY-SA 4.0 |