godofecht / godofecht/flow-scikit
[Bench #478] Make fit/predict/transform/proba separate first-class performance rows
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- Dominant language
- Python
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Description
Parent: #478
The canonical headline currently classifies an estimator/dataset from fit+predict combined time. That hides phase-local wins and losses: GaussianNB Digits fits faster but predicts slower; RandomForest Digits predicts faster but fits slower; KernelRidge loses in both; KernelSVC has different fit/predict behavior.
Generate machine-readable operation rows for every supported estimator operation: fit, predict, transform, decision_function and predict_proba where applicable. Headline estimator totals may remain as a secondary view, but no total may hide a losing operation.
Acceptance: operation-level median/IQR/speedup/status in frozen artifacts and Pages; operation parity/state prerequisites; each loss links to a focused issue; #478 exit criteria are evaluated over operations, not only estimator totals.
Contributor guide
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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 benchmark work tracked in #478 and trace how canonical estimator totals flow into frozen artifacts and Pages. Review the supported-operation and state-prerequisite paths for fit, predict, transform, decision_function, and applicable predict_proba. Done means operation-level median, IQR, speedup, status, parity, and loss links are represented and the #478 exit criteria are evaluated per operation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, scikit-learn
- Domain
- machine-learning, performance
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Active
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100