probabl-ai/skore
Apr 2025 – Nov 2025
4 Months active
Languages Used
PythonJupyter NotebookSQL
Technical Skills
Data AnalysisMachine LearningPandasPythonCode CleanupData Visualization
waridrox
PROFILE
Waridrox
Mohd Warid contributed to the probabl-ai/skore repository by building and refining data analysis and model evaluation features over four months. He enhanced performance metric dashboards by standardizing time unit displays and refactored the MetricsAccessor for consistency. Warid developed confusion matrix visualization and export capabilities, improving interpretability for stakeholders, and overhauled the confusion matrix workflow for maintainability. He addressed data pipeline reliability by ensuring DataFrame column names were string-typed, preventing downstream errors. His work involved Python, Pandas, and Matplotlib, with a focus on code clarity, robust unit testing, and collaborative code reviews, resulting in more reliable and maintainable analytics tooling.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
6Total
Bugs
3
Commits
6
Features
3
Lines of code
1,232
Activity Months4
Work History
November 2025
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for probabl-ai/skore focusing on business value and technical achievements. Delivered a targeted enhancement to model evaluation tooling with a robust refactor of the confusion matrix workflow. This work improves interpretability for stakeholders and accelerates metric-based decision making.
1 Commits • 1 Features
Nov 1, 2025November 2025 monthly summary for probabl-ai/skore focusing on business value and technical achievements. Delivered a targeted enhancement to model evaluation tooling with a robust refactor of the confusion matrix workflow. This work improves interpretability for stakeholders and accelerates metric-based decision making.
November 2025
October 2025
1 Commits
Oct 1, 2025October 2025: Focused data handling improvement in probabl-ai/skore; implemented a bug fix to ensure DataFrame column names are treated as strings to prevent regex-related errors and downstream compatibility issues, improving data pipeline reliability and maintainability.
October 2025
1 Commits
Oct 1, 2025October 2025: Focused data handling improvement in probabl-ai/skore; implemented a bug fix to ensure DataFrame column names are treated as strings to prevent regex-related errors and downstream compatibility issues, improving data pipeline reliability and maintainability.
May 2025
3 Commits • 1 Features
May 1, 2025May 2025 (2025-05) – probabl-ai/skore: Delivered key analytics enhancements and stability fixes that improve data-driven decision making and model evaluation reliability.
3 Commits • 1 Features
May 1, 2025May 2025 (2025-05) – probabl-ai/skore: Delivered key analytics enhancements and stability fixes that improve data-driven decision making and model evaluation reliability.
May 2025
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 — probabl-ai/skore: Delivered enhanced timing metrics display with a time unit suffix, refactored MetricsAccessor for consistent time-unit presentation across modules, and updated tests to align with the new naming convention. No major bugs reported this month; focus on feature delivery and test stabilization. Business value: clearer performance dashboards, faster triage, and more reliable SLO tracking. Technologies demonstrated: metrics instrumentation, cross-module refactor, test-driven updates, and Git-based traceability (commit 5bc3f59b024cf5ae1474ed42c0e4ff5b3af45d12).
April 2025
1 Commits • 1 Features
Apr 1, 2025April 2025 — probabl-ai/skore: Delivered enhanced timing metrics display with a time unit suffix, refactored MetricsAccessor for consistent time-unit presentation across modules, and updated tests to align with the new naming convention. No major bugs reported this month; focus on feature delivery and test stabilization. Business value: clearer performance dashboards, faster triage, and more reliable SLO tracking. Technologies demonstrated: metrics instrumentation, cross-module refactor, test-driven updates, and Git-based traceability (commit 5bc3f59b024cf5ae1474ed42c0e4ff5b3af45d12).
Activity
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Quality Metrics
Correctness96.6%
Maintainability93.4%
Architecture86.6%
Performance80.0%
AI Usage23.4%
Skills & Technologies
Programming Languages
Jupyter NotebookPythonSQL
Technical Skills
Code CleanupData AnalysisData VisualizationDecorator PatternMachine LearningMatplotlibModel EvaluationPandasPythonRefactoringScikit-learnTestingUnit Testingdata manipulationdata visualization
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