Add options for handling zero-inflated datasets
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- Dominant language
- Python
- Stars
- 2
- Forks
- 1
- Avg merge
- 4d 21h
- Merged PRs (30d)
- 1
Description
When testing the new synthetic dataset that is available in PR #2 , I found that the existing generators struggled to handle zero-inflated datasets. In this case, the typical failure mode was for the model to land on a degenerate solution of all zero values.
I've run into a similar problem for a dataset on another project. The fix that we applied in this case was to split the model output head into two parts:
1) A classifier that predicts whether the absolute value is greater than a user-supplied threshold
2) A regressor that will only predict the amplitude for non-zero cells
Contributor guide
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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 by reviewing PR #2 and the existing dataset generators to understand how the synthetic dataset exposes zero-inflated behavior. Define how user-supplied thresholds and the classifier/regressor split should be configured, then validate that zero-inflated datasets no longer produce an all-zero degenerate solution.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100