scikit-learn / scikit-learn/scikit-learn
Cross-validation without copying
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- Dominant language
- Python
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Description
At the moment cross-validation requires indexing an array (or sparse matrix or dataframe) in a way that copies the selected subset of the data. If memory is an issue relative to computation (particular for data being processed in parallel), we could consider modes in which:
- the cross-validation samples are merely weightings over the original dataset (weight=1 where the instance is included in the sample, 0 if excluded), rather than extractions from it.
partial_fitis called over contiguous subsets of the data. Apart from only applying to some estimators, this is complicated by the need to specify multiple iterations withpartial_fit.
thoughts?
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 reviewing scikit-learn's cross-validation behavior and the estimator partial_fit API, focusing on how samples are currently indexed and copied. Compare the proposed weighting and contiguous-subset approaches, then clarify which estimators and iteration semantics are supported before defining what a completed feature would guarantee.
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
- Needs clarification
- Newbie friendliness
- 25/100