lenskit / lenskit/lenskit-codex
Save best model from hyperparameter tuning
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- Dominant language
- Python
- Stars
- 1
- Forks
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Description
We don't need to re-train the best model from hyperparameter tuning for the final evaluation - we should reuse the same saved model.
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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 locating the Python entry points for hyperparameter tuning and final evaluation, then trace where the best model is saved and where evaluation retrains it. Update the flow so final evaluation loads the saved best model, and verify that the evaluation uses that model without training again.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 2/5
- Estimated time
- 1-3 hours
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 68/100