scikit-learn/scikit-learn
Apr 2026 – Apr 2026
1 Month active
Languages Used
Python
Technical Skills
Pythondata sciencemachine learningunit testing
Chani Fainendler
PROFILE
Chani Fainendler
Chani contributed to the scikit-learn/scikit-learn repository by enhancing the reliability of the StratifiedGroupKFold cross-validation tool. She implemented a safety validation in Python to ensure that a clear ValueError is raised when the number of splits exceeds the number of unique groups, addressing a subtle edge case that previously led to runtime errors. Chani also updated and expanded the unit tests to cover this scenario, improving the robustness of the test suite. Her work focused on data science and machine learning workflows, maintaining API compatibility while reducing user debugging time and ensuring more predictable behavior for grouped stratified data.
Overall Statistics
Feature vs Bugs
0%Features
Repository Contributions
1Total
Bugs
1
Commits
1
Features
0
Lines of code
15
Activity Months1
Work History
April 2026
1 Commits
Apr 1, 2026April 2026 monthly summary for scikit-learn/scikit-learn: Delivered a safety validation for StratifiedGroupKFold to raise a clear ValueError when n_splits exceeds the number of unique groups, preventing runtime errors during cross-validation. Updated unit tests to cover the edge case and validate behavior. This enhancement improves reliability of cross-validation for grouped stratified data and reduces user downtime due to cryptic errors. Work was performed on scikit-learn/scikit-learn. The change maintains API compatibility with no breaking changes. Key commit: 237387b8e71e50d547d9130dd6a147bc83f37596 (FIX: StratifiedGroupKFold errors when n_splits > n_groups (#33176)); Co-authored-by: Jérémie du Boisberranger <jeremie@probabl.ai>
1 Commits
Apr 1, 2026April 2026 monthly summary for scikit-learn/scikit-learn: Delivered a safety validation for StratifiedGroupKFold to raise a clear ValueError when n_splits exceeds the number of unique groups, preventing runtime errors during cross-validation. Updated unit tests to cover the edge case and validate behavior. This enhancement improves reliability of cross-validation for grouped stratified data and reduces user downtime due to cryptic errors. Work was performed on scikit-learn/scikit-learn. The change maintains API compatibility with no breaking changes. Key commit: 237387b8e71e50d547d9130dd6a147bc83f37596 (FIX: StratifiedGroupKFold errors when n_splits > n_groups (#33176)); Co-authored-by: Jérémie du Boisberranger <jeremie@probabl.ai>
April 2026
Activity
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Quality Metrics
Correctness100.0%
Maintainability80.0%
Architecture80.0%
Performance80.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Pythondata sciencemachine learningunit testing
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