probabl-ai/skore
Jan 2025 – Jun 2026
17 Months active
Languages Used
CSSPythonreStructuredTextMakefileRSTcssrstJupyter Notebook
Technical Skills
Code ConsistencyData PersistenceDeprecation ManagementDocumentationFront-end DevelopmentNumPy
Auguste Baum
PROFILE
Auguste Baum
Over 17 months, contributed to the probabl-ai/skore repository by designing and developing robust machine learning reporting and evaluation features. Focused on scalable metrics APIs, cross-validation visualization, and reliable data handling, the work included enhancements to model interpretability, project management, and CI/CD workflows. Leveraged Python, Pandas, and Plotly to implement flexible metrics registration, custom scoring, and interactive visualizations, while maintaining compatibility across evolving Python and scikit-learn versions. Emphasized code quality through refactoring, extensive testing, and documentation improvements, resulting in maintainable APIs and streamlined onboarding. The engineering approach prioritized reproducibility, performance, and clarity, supporting both developer productivity and end-user reliability.
Overall Statistics
Feature vs Bugs
84%Features
Repository Contributions
151Total
Bugs
10
Commits
151
Features
51
Lines of code
41,193
Activity Months17
Your Network
36 peopleWork History
June 2026
19 Commits • 3 Features
Jun 1, 2026June 2026 performance summary for probabl-ai/skore focusing on delivering business value through robust reporting and a scalable metrics framework. Key features were delivered to enhance data handling, reporting reliability, and API clarity. The work reduced manual steps, improved data flow into dashboards, and set up a foundation for future metric customization and performance at scale.
19 Commits • 3 Features
Jun 1, 2026June 2026 performance summary for probabl-ai/skore focusing on delivering business value through robust reporting and a scalable metrics framework. Key features were delivered to enhance data handling, reporting reliability, and API clarity. The work reduced manual steps, improved data flow into dashboards, and set up a foundation for future metric customization and performance at scale.
June 2026
May 2026
12 Commits • 6 Features
May 1, 2026May 2026 — Monthly summary for probabl-ai/skore Key outcomes by area: - Features delivered: - Documentation and UX improvements: clearer issue reporting templates, improved labeling, removal of the blank template; docs enhanced with SEO/meta tags and updated Python version guidance. Commits influenced: d7a6bf4..., cfb364a6..., a107661b... - Git integration and project tracking: ability to retrieve the latest Git commit hash and verify working tree status on Project.put to improve version control awareness. Commit: 6ce0bb74... - Metrics display customization: added verbose_name to the add method in metrics classes to tailor metric display names in reports. Commit: 9d9161d7... - Dependency update and compatibility: upgraded skrub to 0.9 across test requirements and project configuration for compatibility and feature parity. Commit: 30d2d657... - Estimator reporting and metrics API enhancements: compute default score during summarize, support multimetric scorers returning dictionaries, relocate/clarify prediction time metrics, and API attribute naming improvements. Commits: 40320fcb..., e9332b6c3a..., 1738c134..., 87c521ab..., 9ffffd6bcb21... - Internal cleanup: removed custom logger utilities as part of aligning with standard Python logging practices. Commit: 4887a1fe... - Major bugs fixed: - Correctness and reliability of estimator reporting: moved predict time from cache to attribute to ensure accurate timing metrics and reduce stale data in reports. Commit: 1738c1345e1423f9f0442ce331822da6838c516f - Compatibility clarity: updated documentation and templates to reflect current supported Python versions, reducing build/test failures due to outdated guidance. - Overall impact and accomplishments: - Strengthened release engineering and traceability through Git integration, enabling faster diagnosis and version control awareness in project workflows. - Improved reporting accuracy, flexibility, and scalability with enhanced estimator reporting, multimetric support, and customizable metric representations. - Reduced maintenance overhead via internal cleanup and logging standardization, and mitigated risk with dependency upgrades. - Enhanced developer and stakeholder usability through documentation enhancements and SEO improvements, improving issue triage efficiency and discoverability of guidance. - Technologies and skills demonstrated: - Python and data tooling for metrics and estimators, Git integration, and project tracking. - API design and multi-metric scoring concepts. - Documentation, SEO, and Python logging best practices.
May 2026
12 Commits • 6 Features
May 1, 2026May 2026 — Monthly summary for probabl-ai/skore Key outcomes by area: - Features delivered: - Documentation and UX improvements: clearer issue reporting templates, improved labeling, removal of the blank template; docs enhanced with SEO/meta tags and updated Python version guidance. Commits influenced: d7a6bf4..., cfb364a6..., a107661b... - Git integration and project tracking: ability to retrieve the latest Git commit hash and verify working tree status on Project.put to improve version control awareness. Commit: 6ce0bb74... - Metrics display customization: added verbose_name to the add method in metrics classes to tailor metric display names in reports. Commit: 9d9161d7... - Dependency update and compatibility: upgraded skrub to 0.9 across test requirements and project configuration for compatibility and feature parity. Commit: 30d2d657... - Estimator reporting and metrics API enhancements: compute default score during summarize, support multimetric scorers returning dictionaries, relocate/clarify prediction time metrics, and API attribute naming improvements. Commits: 40320fcb..., e9332b6c3a..., 1738c134..., 87c521ab..., 9ffffd6bcb21... - Internal cleanup: removed custom logger utilities as part of aligning with standard Python logging practices. Commit: 4887a1fe... - Major bugs fixed: - Correctness and reliability of estimator reporting: moved predict time from cache to attribute to ensure accurate timing metrics and reduce stale data in reports. Commit: 1738c1345e1423f9f0442ce331822da6838c516f - Compatibility clarity: updated documentation and templates to reflect current supported Python versions, reducing build/test failures due to outdated guidance. - Overall impact and accomplishments: - Strengthened release engineering and traceability through Git integration, enabling faster diagnosis and version control awareness in project workflows. - Improved reporting accuracy, flexibility, and scalability with enhanced estimator reporting, multimetric support, and customizable metric representations. - Reduced maintenance overhead via internal cleanup and logging standardization, and mitigated risk with dependency upgrades. - Enhanced developer and stakeholder usability through documentation enhancements and SEO improvements, improving issue triage efficiency and discoverability of guidance. - Technologies and skills demonstrated: - Python and data tooling for metrics and estimators, Git integration, and project tracking. - API design and multi-metric scoring concepts. - Documentation, SEO, and Python logging best practices.
April 2026
7 Commits • 2 Features
Apr 1, 2026April 2026 monthly summary for probabl-ai/skore focused on delivering a more flexible and maintainable metrics evaluation workflow and strengthening project infrastructure. Highlights include enabling custom metrics registration and sklearn-style scorers in EstimatorReport, consolidating data access, and introducing a MetricRegistry to scale metrics handling. Maintained and modernized the codebase with test infrastructure improvements and Python 3.11 readiness, along with workspace string support for local projects.
7 Commits • 2 Features
Apr 1, 2026April 2026 monthly summary for probabl-ai/skore focused on delivering a more flexible and maintainable metrics evaluation workflow and strengthening project infrastructure. Highlights include enabling custom metrics registration and sklearn-style scorers in EstimatorReport, consolidating data access, and introducing a MetricRegistry to scale metrics handling. Maintained and modernized the codebase with test infrastructure improvements and Python 3.11 readiness, along with workspace string support for local projects.
April 2026
March 2026
11 Commits • 4 Features
Mar 1, 2026March 2026 (2026-03) monthly summary highlighting key features delivered, major bugs fixed, overall impact, and technologies demonstrated. Highlights include a metrics subsystem refactor across EstimatorReport, CrossValidationReport, and ComparisonReport; enforcement of NotImplementedError for unsupported data_source='both' with tests; release 0.14.0 introducing top-level dispatchers and MLflow integration; documentation improvements with changelog and landing page enhancements; and improvements to testing structure and CI.
March 2026
11 Commits • 4 Features
Mar 1, 2026March 2026 (2026-03) monthly summary highlighting key features delivered, major bugs fixed, overall impact, and technologies demonstrated. Highlights include a metrics subsystem refactor across EstimatorReport, CrossValidationReport, and ComparisonReport; enforcement of NotImplementedError for unsupported data_source='both' with tests; release 0.14.0 introducing top-level dispatchers and MLflow integration; documentation improvements with changelog and landing page enhancements; and improvements to testing structure and CI.
February 2026
16 Commits • 4 Features
Feb 1, 2026Concise monthly summary for February 2026 (probabl-ai/skore). Highlights focus on delivering interpretable models, improving project robustness, and strengthening internal quality to support reliability, onboarding, and business readiness.
16 Commits • 4 Features
Feb 1, 2026Concise monthly summary for February 2026 (probabl-ai/skore). Highlights focus on delivering interpretable models, improving project robustness, and strengthening internal quality to support reliability, onboarding, and business readiness.
February 2026
January 2026
13 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary focused on delivering more flexible metrics APIs, strengthening reporting clarity, and improving developer onboarding and maintainability. The team implemented foundational metrics enhancements, improved documentation hygiene, and tightened CI/check processes to boost code quality and onboarding velocity.
January 2026
13 Commits • 2 Features
Jan 1, 2026January 2026 monthly summary focused on delivering more flexible metrics APIs, strengthening reporting clarity, and improving developer onboarding and maintainability. The team implemented foundational metrics enhancements, improved documentation hygiene, and tightened CI/check processes to boost code quality and onboarding velocity.
December 2025
4 Commits • 3 Features
Dec 1, 2025December 2025 monthly summary for probabl-ai/skore focused on delivering key features, improving data integrity, and stabilizing dependencies to enable reliable project pipelines and faster issue resolution. The work emphasizes business value through better issue triage, flexible scoring in metrics, and enforcement of consistent ML task scope across a project, while maintaining compatibility with evolving scikit-learn versions.
4 Commits • 3 Features
Dec 1, 2025December 2025 monthly summary for probabl-ai/skore focused on delivering key features, improving data integrity, and stabilizing dependencies to enable reliable project pipelines and faster issue resolution. The work emphasizes business value through better issue triage, flexible scoring in metrics, and enforcement of consistent ML task scope across a project, while maintaining compatibility with evolving scikit-learn versions.
December 2025
November 2025
9 Commits • 3 Features
Nov 1, 2025November 2025 summary: Delivered unified data_source with value 'both' across EstimatorReports and displays to enable direct train-vs-test comparisons in ROC, precision-recall, prediction_error, and summary metrics. Extended data_source='both' support to RocCurveDisplay, EstimatorReport metrics, and ComparisonReport metrics, establishing consistent cross-dataset reporting and faster diagnostic insight. Fixed a confusion-matrix normalization issue by reverting an unintended metric accessor change, restoring correct display behavior. Improved test infrastructure and formatting, reducing test durations from ~45s to ~12s through simpler models and fixture reuse, and enhancing code readability across tests and fixtures. Demonstrated strong proficiency in Python data-modeling, visualization components, and test optimization, delivering tangible business value through reliable reporting and quicker feedback loops.
November 2025
9 Commits • 3 Features
Nov 1, 2025November 2025 summary: Delivered unified data_source with value 'both' across EstimatorReports and displays to enable direct train-vs-test comparisons in ROC, precision-recall, prediction_error, and summary metrics. Extended data_source='both' support to RocCurveDisplay, EstimatorReport metrics, and ComparisonReport metrics, establishing consistent cross-dataset reporting and faster diagnostic insight. Fixed a confusion-matrix normalization issue by reverting an unintended metric accessor change, restoring correct display behavior. Improved test infrastructure and formatting, reducing test durations from ~45s to ~12s through simpler models and fixture reuse, and enhancing code readability across tests and fixtures. Demonstrated strong proficiency in Python data-modeling, visualization components, and test optimization, delivering tangible business value through reliable reporting and quicker feedback loops.
October 2025
3 Commits • 2 Features
Oct 1, 2025October 2025 (2025-10) focused on user experience, data persistence, and data integrity in probabl-ai/skore. Delivered three key contributions: (1) improved Skore Hub login UX with clearer feedback and robust handling when auto-launch fails; (2) migrated the default local project workspace to the user data directory to ensure persistence across platforms, with accompanying docs/tests updates; (3) enhanced data integrity and efficiency by conditionally uploading permutation data only when computed and by updating the EstimatorReport protocol to include a _hash field. These changes reduce onboarding friction, improve cross-OS reliability, and strengthen reproducibility and traceability in model evaluation.
3 Commits • 2 Features
Oct 1, 2025October 2025 (2025-10) focused on user experience, data persistence, and data integrity in probabl-ai/skore. Delivered three key contributions: (1) improved Skore Hub login UX with clearer feedback and robust handling when auto-launch fails; (2) migrated the default local project workspace to the user data directory to ensure persistence across platforms, with accompanying docs/tests updates; (3) enhanced data integrity and efficiency by conditionally uploading permutation data only when computed and by updating the EstimatorReport protocol to include a _hash field. These changes reduce onboarding friction, improve cross-OS reliability, and strengthen reproducibility and traceability in model evaluation.
October 2025
September 2025
8 Commits • 3 Features
Sep 1, 2025September 2025 monthly summary for probabl-ai/skore focusing on delivering API clarity, robust data handling, enhanced observability, and CI efficiency. Highlights include internal API naming and reporting structure refactors to improve maintainability and reliability, robust data handling and input normalization to ensure consistent train-test splits, observability enhancements for external services via added logging around retry behavior, and CI workflow optimization to reduce test runtime by running pytest serially. Minor documentation improvement also completed to align with code changes.
September 2025
8 Commits • 3 Features
Sep 1, 2025September 2025 monthly summary for probabl-ai/skore focusing on delivering API clarity, robust data handling, enhanced observability, and CI efficiency. Highlights include internal API naming and reporting structure refactors to improve maintainability and reliability, robust data handling and input normalization to ensure consistent train-test splits, observability enhancements for external services via added logging around retry behavior, and CI workflow optimization to reduce test runtime by running pytest serially. Minor documentation improvement also completed to align with code changes.
August 2025
1 Commits • 1 Features
Aug 1, 2025Month 2025-08: Delivered Cross-Validation Visualization in ModelExplorerWidget for probabl-ai/skore, introducing a parallel coordinates plot to display cross-validation reports and compare performance metrics across ML tasks and report types. This feature aligns with backlog item #1980 and was implemented via a focused commit, enhancing product analytics and user guidance.
1 Commits • 1 Features
Aug 1, 2025Month 2025-08: Delivered Cross-Validation Visualization in ModelExplorerWidget for probabl-ai/skore, introducing a parallel coordinates plot to display cross-validation reports and compare performance metrics across ML tasks and report types. This feature aligns with backlog item #1980 and was implemented via a focused commit, enhancing product analytics and user guidance.
August 2025
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for probabl-ai/skore. Focused on feature deliveries that strengthen model evaluation workflows and CI reliability in production-like environments. Delivered Cross-Validation Prediction Error Visualization, including refactoring of PredictionErrorDisplay to support aggregated data from multiple estimators or CV splits, plus extensive tests to ensure robustness. Implemented CI compatibility upgrades to add Python 3.13 support and refined inclusions/exclusions for specific scikit-learn versions to maintain compatibility. No explicit major bug fixes were recorded this month; emphasis was on delivering robust features and improving CI stability. Overall impact: improved evaluation diagnostics for CV experiments, smoother integration of latest Python/scikit-learn stacks, and stronger test coverage driving reliability. Technologies/skills demonstrated: Python, test-driven development, refactoring for multi-estimator data, data visualization integration, CI workflow management, and cross-version compatibility.
June 2025
2 Commits • 2 Features
Jun 1, 2025June 2025 monthly summary for probabl-ai/skore. Focused on feature deliveries that strengthen model evaluation workflows and CI reliability in production-like environments. Delivered Cross-Validation Prediction Error Visualization, including refactoring of PredictionErrorDisplay to support aggregated data from multiple estimators or CV splits, plus extensive tests to ensure robustness. Implemented CI compatibility upgrades to add Python 3.13 support and refined inclusions/exclusions for specific scikit-learn versions to maintain compatibility. No explicit major bug fixes were recorded this month; emphasis was on delivering robust features and improving CI stability. Overall impact: improved evaluation diagnostics for CV experiments, smoother integration of latest Python/scikit-learn stacks, and stronger test coverage driving reliability. Technologies/skills demonstrated: Python, test-driven development, refactoring for multi-estimator data, data visualization integration, CI workflow management, and cross-version compatibility.
May 2025
10 Commits • 2 Features
May 1, 2025May 2025: Enhanced cross-validation reporting and plotting in probabl-ai/skore. Implemented ROC and precision-recall curve plotting for cross-validation comparison reports with per-estimator curves, improved plotting API, and ensured chance level is displayed in legends. Refactored plotting internals to non-static methods and removed the ax parameter to simplify usage. Also delivered maintenance work: fixes to documentation links, prevented in-place mutations of cached DataFrames, and corrected a readability typo in the metrics error message. Extended Python compatibility to 3.13 by removing the upper bound in package configs, positioning the project for future Python versions and smoother onboarding.
10 Commits • 2 Features
May 1, 2025May 2025: Enhanced cross-validation reporting and plotting in probabl-ai/skore. Implemented ROC and precision-recall curve plotting for cross-validation comparison reports with per-estimator curves, improved plotting API, and ensured chance level is displayed in legends. Refactored plotting internals to non-static methods and removed the ax parameter to simplify usage. Also delivered maintenance work: fixes to documentation links, prevented in-place mutations of cached DataFrames, and corrected a readability typo in the metrics error message. Extended Python compatibility to 3.13 by removing the upper bound in package configs, positioning the project for future Python versions and smoother onboarding.
May 2025
April 2025
11 Commits • 3 Features
Apr 1, 2025April 2025 focused on strengthening reporting, typing discipline, and CI visibility in probabl-ai/skore. Delivered cross-report feature parity in Cross-Validation reporting, standardized type hints for positive labels and aggregation, hardened prediction-time casting to address pandas deprecations, stabilized progress bar behavior for nested reports, and updated CI to emit XML coverage reports. These changes improve evaluation consistency, developer experience, and CI feedback loops, enabling safer feature launches and clearer user guidance.
April 2025
11 Commits • 3 Features
Apr 1, 2025April 2025 focused on strengthening reporting, typing discipline, and CI visibility in probabl-ai/skore. Delivered cross-report feature parity in Cross-Validation reporting, standardized type hints for positive labels and aggregation, hardened prediction-time casting to address pandas deprecations, stabilized progress bar behavior for nested reports, and updated CI to emit XML coverage reports. These changes improve evaluation consistency, developer experience, and CI feedback loops, enabling safer feature launches and clearer user guidance.
March 2025
11 Commits • 3 Features
Mar 1, 2025March 2025 for probabl-ai/skore focused on delivering model interpretability enhancements, documentation and UX improvements, and developer experience refinements. Implemented EstimatorReport enhancements including feature permutation importance, mean decrease impurity metrics, and timing metrics, providing deeper insights into model behavior and performance. Strengthened product quality through documentation and API clarity: improved docs, docstrings, error messages, and consistent structure across config/API docs. Invested in tooling and testing to improve reproducibility and developer experience: refined pre-commit, deterministic plotting behavior (avoid caching when random_state is None), added xdoctest support, and implemented a robust __repr__ for project objects. These changes collectively reduce troubleshooting time, accelerate onboarding, and empower users with actionable insights while improving the maintainability and reliability of the codebase.
11 Commits • 3 Features
Mar 1, 2025March 2025 for probabl-ai/skore focused on delivering model interpretability enhancements, documentation and UX improvements, and developer experience refinements. Implemented EstimatorReport enhancements including feature permutation importance, mean decrease impurity metrics, and timing metrics, providing deeper insights into model behavior and performance. Strengthened product quality through documentation and API clarity: improved docs, docstrings, error messages, and consistent structure across config/API docs. Invested in tooling and testing to improve reproducibility and developer experience: refined pre-commit, deterministic plotting behavior (avoid caching when random_state is None), added xdoctest support, and implemented a robust __repr__ for project objects. These changes collectively reduce troubleshooting time, accelerate onboarding, and empower users with actionable insights while improving the maintainability and reliability of the codebase.
March 2025
February 2025
8 Commits • 5 Features
Feb 1, 2025February 2025 (Month: 2025-02) – Focused on reliability, performance, and developer productivity for the probabl-ai/skore repository. Delivered robust cross-validation reporting with graceful interruption handling and unit tests, enabling uninterrupted model evaluation and progress visibility. Extended ML task detection to support multi-output targets and non-numpy arrays, broadening applicability of automated task classification and reducing edge-case failures. Improved performance and repeatability with a caching mechanism for sub-estimator predictions in ComparisonReport, including a clear cache invalidation path. Added safe project deletion via Project.clear delete_project parameter, reducing operational risk when cleaning up projects. Invested in documentation tooling to speed builds and improve API navigation, accelerating onboarding and collaboration.
February 2025
8 Commits • 5 Features
Feb 1, 2025February 2025 (Month: 2025-02) – Focused on reliability, performance, and developer productivity for the probabl-ai/skore repository. Delivered robust cross-validation reporting with graceful interruption handling and unit tests, enabling uninterrupted model evaluation and progress visibility. Extended ML task detection to support multi-output targets and non-numpy arrays, broadening applicability of automated task classification and reducing edge-case failures. Improved performance and repeatability with a caching mechanism for sub-estimator predictions in ComparisonReport, including a clear cache invalidation path. Added safe project deletion via Project.clear delete_project parameter, reducing operational risk when cleaning up projects. Invested in documentation tooling to speed builds and improve API navigation, accelerating onboarding and collaboration.
January 2025
6 Commits • 3 Features
Jan 1, 2025January 2025 monthly summary for probabl-ai/skore: Restored default non-HTML display behavior by reverting the HTML representation of Items across environments (Jupyter compatibility restored). Cleaned up and standardized the CrossValidation reporting API to reduce user confusion by promoting EstimatorReport/CrossValidationReport under a unified 'report' terminology and deprecating CrossValidationReporter. Improved documentation UX by reducing clutter (narrower left sidebar) and removing type hints from API docs for cleaner presentation. Implemented a storage optimization for NumpyArrayItem by switching from array.tolist() to numpy.save while preserving allow_pickle=False. These changes reduce maintenance burden, improve developer experience, and enhance performance and API clarity.
6 Commits • 3 Features
Jan 1, 2025January 2025 monthly summary for probabl-ai/skore: Restored default non-HTML display behavior by reverting the HTML representation of Items across environments (Jupyter compatibility restored). Cleaned up and standardized the CrossValidation reporting API to reduce user confusion by promoting EstimatorReport/CrossValidationReport under a unified 'report' terminology and deprecating CrossValidationReporter. Improved documentation UX by reducing clutter (narrower left sidebar) and removing type hints from API docs for cleaner presentation. Implemented a storage optimization for NumpyArrayItem by switching from array.tolist() to numpy.save while preserving allow_pickle=False. These changes reduce maintenance burden, improve developer experience, and enhance performance and API clarity.
January 2025
Activity
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Quality Metrics
Correctness96.8%
Maintainability92.2%
Architecture92.4%
Performance89.4%
AI Usage23.4%
Skills & Technologies
Programming Languages
CSSHTMLJupyter NotebookMakefileMarkdownPythonRSTTOMLYAMLcss
Technical Skills
API DesignAPI DevelopmentAPI designAPI developmentBackend DevelopmentBug FixBuild SystemsCI/CDCSSCachingClient DevelopmentCode ConsistencyCode QualityCode RefactoringConfiguration Management
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