Can we compare model.score_ values across different model types? (DML, DRL, linear vs forests, etc.)
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Description
I'm getting differences in .score_ values of 10x, between simple data sets and small models using LinearDRLearner vs LinearDML. So I'm seeing 21,000 for LinearDRLeaner and 2,000 for LinearDML. Is it logical to compare scores this way or only logical within a given model type across different parameters?
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Research direction
Start by reading the score_ behavior for LinearDRLearner and LinearDML, focusing on how each model type defines and reports the value. Compare the two meanings and determine whether cross-model comparisons are valid; document the conclusion and any within-model comparison guidance.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 30/100