Parameter tuning for Policy Learning
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Description
I would like to start off by saying good job on coming up with the Econml package. It has definitely been a useful tool to my data science toolkit. I have one question about the policy learning models that I was hoping to get some clarification on.
For the policy learning models available in econml (DRPolicyForest, DRPolicyTree, etc), I noticed that there is no score method available. My question is, how am I able to determine the best parameters eg, max_depth, n_estimators in the case of PolicyForest, if there is no score for me to evaluate against? Is there another way to determine the right parameter values?
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- Read the whole issue, then the project's contributing guide.
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Research direction
Review the policy learning models named in the issue, including DRPolicyForest and DRPolicyTree, and their max_depth and n_estimators parameters. Determine how parameter selection should be evaluated when no score method is available; the issue does not name specific files or tests, so completion criteria would need to be established first.
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
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100