hyperparameters tuning
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Description
In FAQ, you mention that hyperparameters can be tuned using all the data:
"How do I select the hyperparameters of the first stage models?
Alternatively, you can pick the best first stage models outside of the EconML framework and pass in the selected models to EconML. This can save on runtime and computational resources. Furthermore, it is statistically more stable since all data is being used for hyper-parameter tuning rather than a single fold inside of the DML algorithm (as long as the number of hyperparameter values that you are selecting over is not exponential in the number of samples, this approach is statistically valid)."
We are trying to understand the statistical validity of this approach. Can you please point us to the right papers?
Thank you.
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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 with the hyperparameter-tuning passage quoted from the FAQ and review the statistical-validity question in the issue. Identify relevant papers for tuning first-stage models with all the data, then update the FAQ with citations and an explanation that answers the question.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100