scikit-learn-contrib/MAPIE
Nov 2025 – Nov 2025
1 Month active
Languages Used
PythonreStructuredTextrst
Technical Skills
Code quality assuranceDependency managementPythonPython developmentdata sciencedocumentation
HannesMK
PROFILE
Hannesmk
In November 2025, Han Feng contributed to the scikit-learn-contrib/MAPIE repository by enhancing the robustness of BlockBootstrap cross-validation, ensuring all non-training samples are included in test sets and that incomplete blocks use the last available samples. This update, implemented in Python, improved the reliability of model evaluation and included comprehensive test revisions to prevent regressions. Han also upgraded development tooling by adding Ruff for code quality assurance and updated project documentation and contributor records using reStructuredText. These contributions demonstrated depth in dependency management, documentation, and machine learning workflows, supporting both maintainability and collaborative development within the MAPIE project.
Overall Statistics
Feature vs Bugs
67%Features
Repository Contributions
5Total
Bugs
1
Commits
5
Features
2
Lines of code
28
Activity Months1
Your Network
17 peopleShared Repositories
17
Work History
November 2025
5 Commits • 2 Features
Nov 1, 2025November 2025 MAPIE contributions focused on robustness, quality, and documentation to support reliable model evaluation and scalable collaboration. Delivered a BlockBootstrap cross-validation fix that ensures test sets include all non-training samples and uses the last samples when blocks are incomplete, with tests updated to prevent regressions. Updated project documentation and contributor records to reflect recent contributions. Upgraded development tooling by adding Ruff to dev dependencies to improve code quality checks during development. These changes enhance evaluation reliability, developer productivity, and project maintainability, aligning with our goals of robust ML validation and efficient collaboration.
5 Commits • 2 Features
Nov 1, 2025November 2025 MAPIE contributions focused on robustness, quality, and documentation to support reliable model evaluation and scalable collaboration. Delivered a BlockBootstrap cross-validation fix that ensures test sets include all non-training samples and uses the last samples when blocks are incomplete, with tests updated to prevent regressions. Updated project documentation and contributor records to reflect recent contributions. Upgraded development tooling by adding Ruff to dev dependencies to improve code quality checks during development. These changes enhance evaluation reliability, developer productivity, and project maintainability, aligning with our goals of robust ML validation and efficient collaboration.
November 2025
Activity
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Quality Metrics
Correctness96.0%
Maintainability96.0%
Architecture96.0%
Performance96.0%
AI Usage20.0%
Skills & Technologies
Programming Languages
PythonreStructuredTextrst
Technical Skills
Code quality assuranceDependency managementPythonPython developmentdata sciencedocumentationmachine learningversion control
Repositories Contributed To
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