scikit-learn / scikit-learn/scikit-learn
Pass the confusion matrix as a parameter
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- Dominant language
- Python
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Description
Describe the workflow you want to enable
Functions like sklearn.metrics.balanced_accuracy_score, f1_score, jaccard_score, matthews_corrcoef, etc. recalculate the confusion matrix every time, which is very time consuming. I would want to calculate it once and then pass it to these functions as a parameter to get the resulting quality metrics much faster. If you want to calculate more than one of these scores on the same big data set this will save a lot of time.
Describe your proposed solution
Check if the parameter "cm" is passed and then skip its recalculation if yes.
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 with the named sklearn.metrics functions: balanced_accuracy_score, f1_score, jaccard_score, and matthews_corrcoef, and inspect how each currently calculates its confusion matrix. Define the shared parameter behavior and verify that passing a precomputed matrix avoids recalculation while preserving the existing metric 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