scikit-learn / scikit-learn/scikit-learn
More details about the Crammer-Singer method for LinearSVM
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Description
Description
On the current page about the LinearSVC model, we are given the option to use either OVR or Crammer-Singer. Though the former is a well-known method, it seems that Crammer-Singer is a very specific algorithm developed for SVMs. The page talks about the nicer theoretical foundations of the method, as well as the longer training time. However, the information about the method is a bit sparse (more about it here, but it says basically the same thing). It would be nice to have:
- A link to the original paper
- A general overview of the differences between Crammer-Singer and OVR/OVO
- A benchmark comparison between the methods above, in terms of computation time and accuracy, on varying dataset sizes
- An explanation of the important formulas from the paper
Versions
scikit-learn 0.20.3
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the LinearSVC API page and the SVM user guide section referenced in the issue, then read the linked Crammer-Singer paper. Define the comparison scope and benchmark datasets before documenting the method, its formulas, and differences from OVR/OVO; done means the documentation includes the paper link, explanation, and reproducible comparison results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100