PolicyEngine / PolicyEngine/snap-qc-sim
Error model on intermediates: train FY2017-23, hold out FY2024
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- Dominant language
- Python
- Stars
- 0
- Forks
- 0
- Avg merge
- 18m
- Merged PRs (30d)
- 5
Description
P(error, category, magnitude | intermediates, covariates), starting from existing rule-mining/gradient-boosting baselines; publish lift vs the no-intermediates baseline. docs/v2-error-model.md §2.
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Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with docs/v2-error-model.md §2, then locate the existing rule-mining and gradient-boosting baselines. Train the error model on FY2017–23 with intermediates and covariates, hold out FY2024, and publish lift against the no-intermediates baseline.
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
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 45/100