Variable selection for final model
- Dominant language
- R
- Stars
- 1
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
- Define different variable sets, and test the effect of these decisions on final BGC projections.
- [ ] Baseline simple variable set (Tmax/Tmin/PPT)
- [ ] Expert set of variables included in Courtney's most recent models (selected by Will)
- [ ] Local variable selection
- [ ] Kitchen sink approach - ALL variables
- [ ] Sensitivity analysis to see how much it matters which variables are included
Contributor guide
No contributing guide indexed for this repository
Research direction
No files, tests, or entry points are named. Start by locating the existing BGC projection workflow, then map where variable sets enter the final model. Done means the baseline, expert, local-selection, and kitchen-sink sets have been tested and their projection effects compared through a sensitivity analysis.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- r
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100