
Faustin Pulveric contributed to the scikit-learn-contrib/MAPIE repository by leading API modernization, documentation refinement, and risk-control feature development over six months. He standardized API interfaces and naming conventions, migrated tutorials and examples to the v1 API, and improved onboarding through comprehensive migration guides. Faustin enhanced risk-control documentation, clarifying the applicability of methods like CRC, RCPS, and LTT, and restructured the test suite for greater reliability. His work involved Python, Sphinx, and Jupyter Notebook, emphasizing code consistency, CI/CD, and technical writing. These efforts improved maintainability, reduced onboarding friction, and enabled safer integration of MAPIE’s conformal prediction tools in production.

September 2025 MAPIE monthly summary focusing on risk-control documentation improvements to support clearer usage and safer integration in production. Key capture: Strengthened documentation around risk control by explaining applicability to metrics beyond precision (e.g., recall) and by distinguishing risk-control methods (CRC, RCPS, LTT) based on their underlying assumptions and guarantees, improving clarity and accuracy of the documented methodologies.
September 2025 MAPIE monthly summary focusing on risk-control documentation improvements to support clearer usage and safer integration in production. Key capture: Strengthened documentation around risk control by explaining applicability to metrics beyond precision (e.g., recall) and by distinguishing risk-control methods (CRC, RCPS, LTT) based on their underlying assumptions and guarantees, improving clarity and accuracy of the documented methodologies.
MAPIE monthly summary for 2025-08: Focused on documentation quality and test stability, delivering clearer guidance for risk-control features and strengthening test reliability across versions.
MAPIE monthly summary for 2025-08: Focused on documentation quality and test stability, delivering clearer guidance for risk-control features and strengthening test reliability across versions.
MAPIE Monthly Summary - July 2025: Delivered a critical bug fix for double inference in predict_set and completed a focused round of documentation and compatibility updates to enhance reliability, onboarding, and release readiness. Key work included updating HISTORY and release notes, aligning docstrings with implementation, removing outdated setup steps, and updating Python version support. These improvements reduce prediction errors, improve user clarity, and streamline future releases.
MAPIE Monthly Summary - July 2025: Delivered a critical bug fix for double inference in predict_set and completed a focused round of documentation and compatibility updates to enhance reliability, onboarding, and release readiness. Key work included updating HISTORY and release notes, aligning docstrings with implementation, removing outdated setup steps, and updating Python version support. These improvements reduce prediction errors, improve user clarity, and streamline future releases.
June 2025 MAPIE monthly summary: Documentation-focused month delivering reader-focused improvements, improved accessibility and risk management guidance for users integrating MAPIE with LLMs. Key outcomes include a dark-mode rendering fix for the educational visual, expanded LLM risk control guidance in documentation (FAQs, guardrails, conformal predictions, and references), and clearer release notes and README examples to support onboarding and safe usage. These efforts reduce onboarding time, increase user confidence, and reinforce MAPIE's commitment to safe, auditable risk control tooling.
June 2025 MAPIE monthly summary: Documentation-focused month delivering reader-focused improvements, improved accessibility and risk management guidance for users integrating MAPIE with LLMs. Key outcomes include a dark-mode rendering fix for the educational visual, expanded LLM risk control guidance in documentation (FAQs, guardrails, conformal predictions, and references), and clearer release notes and README examples to support onboarding and safe usage. These efforts reduce onboarding time, increase user confidence, and reinforce MAPIE's commitment to safe, auditable risk control tooling.
May 2025 MAPIE monthly summary: The team focused on delivering a clear, stable v1 API experience through comprehensive migrations, API standardization, and documentation improvements, enabling smoother adoption and reducing long-term maintenance. The work enhances developer experience and business value by aligning tutorials, examples, and docs with the v1 API, reducing onboarding friction for users and contributors, and strengthening API stability for downstream projects.
May 2025 MAPIE monthly summary: The team focused on delivering a clear, stable v1 API experience through comprehensive migrations, API standardization, and documentation improvements, enabling smoother adoption and reducing long-term maintenance. The work enhances developer experience and business value by aligning tutorials, examples, and docs with the v1 API, reducing onboarding friction for users and contributors, and strengthening API stability for downstream projects.
April 2025 MAPIE development focused on API consistency, v1 compatibility, and documentation-driven migration readiness across the scikit-learn-contrib/MAPIE repository. Key features delivered include API naming and interface alignment with MAPIE v1 (confidence_level usage, regression_coverage_score rename), TimeSeries API refactor, and multi-level support in mean width calculations. Mondrian API modernization was completed (removing MondrianCP and adopting SplitConformalRegressor) and tutorials updated to clarify group-conditional coverage. Comprehensive documentation improvements and a v1 migration guide were released to reduce onboarding friction and improve maintainability.
April 2025 MAPIE development focused on API consistency, v1 compatibility, and documentation-driven migration readiness across the scikit-learn-contrib/MAPIE repository. Key features delivered include API naming and interface alignment with MAPIE v1 (confidence_level usage, regression_coverage_score rename), TimeSeries API refactor, and multi-level support in mean width calculations. Mondrian API modernization was completed (removing MondrianCP and adopting SplitConformalRegressor) and tutorials updated to clarify group-conditional coverage. Comprehensive documentation improvements and a v1 migration guide were released to reduce onboarding friction and improve maintainability.
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