scikit-learn / scikit-learn/scikit-learn
Possible to parallelize sklearn.impute.KNNImputer?
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Description
Greetings!
Do you think it might be possible to parallelize the algorithm for sklearn.impute.KNNImputer in the future?
scikit-learn's implementation of sklearn.neighbors.KNeighborsClassifier accepts an n_jobs parameter to achieve this, but the corresponding imputation function does not and can be quite slow for large datasets.
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 by comparing the KNNImputer implementation with sklearn.neighbors.KNeighborsClassifier, focusing on how the latter exposes n_jobs. Determine the supported parallelization behavior and its API implications for large datasets. Done means KNNImputer can parallelize its work through a documented, tested interface without changing imputation results.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100