FOP: Build models using new features
- Dominant language
- Python
- Stars
- 65
- Forks
- 11
- Avg merge
- 21h 25m
- Merged PRs (30d)
- 70
Description
**Describe the task**
Build, train, and evaluate models using new features.
**Acceptance Criteria**
- [ ] Understand what features are useful for fire occurrence prediction in BC.
- [ ] Quantify the accuracy of predictions.
- [ ] Explore if there are different geospatial contexts for training FOP models throughout the province.
**Additional context**
- Add any other context about the task here.
- Or here
Contributor guide
Research direction
The issue names no files, tests, or entry points. Start by locating the existing fire occurrence prediction model and its training and evaluation data, then determine which features and geographic contexts are available. Done means documenting useful features, quantified prediction accuracy, and any province-wide geospatial differences in model training.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100