FOP: Presentation of model features and skill
- Dominant language
- Python
- Stars
- 65
- Forks
- 11
- Avg merge
- 21h 25m
- Merged PRs (30d)
- 70
Description
**Describe the task**
Develop tools to visualize how FOP models work and perform for non-technical users.
**Acceptance Criteria**
- [ ] Ability to present what features used to train a model and explain their contribution to predictions.
- [ ] Ability to compare and quantify the accuracy and skill of different models, eg.) confusion matrix.
- [ ] Ability to show how model performance varies geographically throughout the province and for different times of year and conditions.
**Additional context**
- I do not expect a model to perform the same in each of the Fire Centres or across different seasons. We need to consider these geo-spatial factors when evaluating models.
Contributor guide
Research direction
No files, tests, or entry points are named. Start by locating the FOP model evaluation and presentation components, then define how feature contributions, model accuracy, confusion matrices, and geographic or seasonal performance should be represented. Done requires agreed user-facing tools covering all three acceptance criteria.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning
- Domain
- data-visualization, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100