Smoothed Hinge Loss analysis and refactor
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- Dominant language
- Terra
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- 795
- Forks
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Description
Smoothed Hinge Loss is a bit of a black sheep currently. It was introduced as an experiment with the goal of approximating an SVM using a GLM with a modified Hinge Loss function. However, it's currently uncertain how well the function converges. In addition, it's treated a bit differently in the code than the other functions (ex. normalization is ignored for it). There are no current use cases for it.
We need to take a closer look at Smoothed Hinge Loss and its future within Photon.
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
The issue does not name files, tests, or an entry point. Locate the Smoothed Hinge Loss implementation and compare it with the other loss functions, especially normalization handling. Review its convergence behavior and current use cases, then document whether it should be retained and what refactoring is required.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- machine-learning
- Domain
- machine-learning
- Issue type
- Refactor
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100