scikit-learn / scikit-learn/scikit-learn
Implementing partial_fit for NearestCentroid
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Description
Description
The NearestCentroid classifier (https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.NearestCentroid.html) does not currently have a partial_fit method, but it would be very useful when running the algorithm in an online fashion. The math for implementing it is simple; it's just a matter of updating the centroids weighted by the number of samples. The implementation could be similar to the partial_fit method of the GaussianNB classifier (except for the obvious mathematical differences). I'm happy to submit an implementation of this, but I wanted to gather input first to see if others have already done this and whether others would find this useful.
Steps/Code to Reproduce
Look at NearestCentroid classifier code and notice that it has not partial_fit method.
Expected Results
There should be a partial_fit method.
Actual Results
There is no partial_fit method.
Versions
N/A
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
Review the NearestCentroid classifier code and compare it with GaussianNB's partial_fit implementation. Determine the required online centroid updates and API behavior, then verify that partial_fit supports the expected incremental workflow with appropriate tests.
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