scikit-learn / scikit-learn/scikit-learn
Standard attribute for model completion status
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Description
At the moment, there is no way to know why a model stopped fitting (aside from exceptions). It may be because convergence was reached (below a tolerance), or because a hard limit on iterations was hit, or possibly some other condition was met. It would be excellent if Scikit-learn models had some kind of flat attached after fitting. Perhaps model.fit_status_ or similar. This could then be reported during usage.
For example, I'm hitting a problem with MiniBatchKMeans clustering, where some clusters are not assigned. The only think I know is that the max_iter was not met, but I don't know if it's due to the max_no_improvement flag, because the tol is too high (not in my case). It would be great if the model reported this in a standard way.
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
No file, test, or entry point is identified. Start by reviewing the fitting behavior of MiniBatchKMeans, including max_iter, max_no_improvement, and tol, then compare how other estimators report completion. Done means defining and documenting a consistent completion-status attribute that distinguishes the stopping condition.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 25/100