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

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. 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

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