How to use SinglePolicyInterpreter to score on unseen/new data in order to know how to treat?
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Description
@kant @Padarn @kbattocchi Hello, my understanding is that one can use SinglePolicyInterpreter to apply to new instances (unseen data) in order to decide if a treatment shall be given. Is that true?/ If so, then, I wonder if SinglePolicyInterpreter can also be used for a continuous treatment values between 0-10 for example?
I understand that the SinglePolicyInterpreter will decide what the best splits are but can the SinglePolicyInterpreter build a policy around a continuous treatment? That way, I can understand which subjects to give how much treatment? If so, is there any example notebook please?
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Research direction
Start by reviewing the SinglePolicyInterpreter API and any existing examples or notebooks that cover unseen data and treatment values. Determine whether continuous treatments are supported and document the answer with a focused example notebook if the behavior is available; otherwise, clearly record the limitation.
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Assessment
- Tech stack
- jupyter-notebook, python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 32/100