tensorflow / tensorflow/probability
providing sample weights to glm.fit() and glm.fit.sparse()
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Description
Hi
I checked the documentation and there doesn't seem to be a way to provide a vector of sample weights to glm.fit()/glm.fit.sparse() to do weighted regression.
More specifically, I want to perform robust regression by providing huber weights to glm.fit.sparse().
Am I something missing or is there another way I can perform weighted regression?
Thanks
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
The issue names glm.fit() and glm.fit.sparse() as the relevant entry points, but does not identify files or tests. Read the implementations and documentation for these entry points first, then determine how sample weights should support weighted regression; done means the requested weighting path is supported and verified by tests.
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Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 35/100