probabl-ai/skore
Dec 2025 – Jan 2026
2 Months active
Languages Used
Python
Technical Skills
data visualizationmachine learningpandasunit testingPython
Adrien Morel
PROFILE
Adrien Morel
Adrien Morel contributed to the probabl-ai/skore repository by enhancing the reliability and clarity of model evaluation visualizations. He addressed a critical edge case in multiclass precision-recall plotting, resolving issues with string labels by leveraging pandas variable binding and expanding unit test coverage to prevent regressions. In a subsequent feature, Adrien unified training and testing data displays within the ComparisonReport, introducing a data_source column to enable clear, side-by-side metric comparisons across datasets. His work, implemented in Python and utilizing data visualization and machine learning techniques, improved the interpretability and robustness of evaluation plots for both developers and stakeholders.
Overall Statistics
Feature vs Bugs
50%Features
Repository Contributions
2Total
Bugs
1
Commits
2
Features
1
Lines of code
341
Activity Months2
Work History
January 2026
1 Commits • 1 Features
Jan 1, 2026January 2026 — probabl-ai/skore: Feature delivery focusing on unifying training/testing data visuals in ComparisonReport. Implemented data_source='both' to merge training and testing metrics into a single long-form DataFrame with an explicit data_source column, enabling separate subplots for train vs test and clarifying Precision-Recall, ROC, and Prediction Error visuals. This change reduces ambiguity in model evaluation and improves interpretability of cross-dataset performance. Related issues closed: #1874, #2154-2156.
1 Commits • 1 Features
Jan 1, 2026January 2026 — probabl-ai/skore: Feature delivery focusing on unifying training/testing data visuals in ComparisonReport. Implemented data_source='both' to merge training and testing metrics into a single long-form DataFrame with an explicit data_source column, enabling separate subplots for train vs test and clarifying Precision-Recall, ROC, and Prediction Error visuals. This change reduces ambiguity in model evaluation and improves interpretability of cross-dataset performance. Related issues closed: #1874, #2154-2156.
January 2026
December 2025
1 Commits
Dec 1, 2025December 2025 monthly summary for probabl-ai/skore focusing on reliability and accuracy improvements in visualization. Addressed a critical edge case in multiclass precision-recall plotting with string labels, stabilizing the plotting path and strengthening test coverage to prevent regressions.
December 2025
1 Commits
Dec 1, 2025December 2025 monthly summary for probabl-ai/skore focusing on reliability and accuracy improvements in visualization. Addressed a critical edge case in multiclass precision-recall plotting with string labels, stabilizing the plotting path and strengthening test coverage to prevent regressions.
Activity
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Quality Metrics
Correctness100.0%
Maintainability90.0%
Architecture90.0%
Performance90.0%
AI Usage40.0%
Skills & Technologies
Programming Languages
Python
Technical Skills
Pythondata visualizationmachine learningpandasunit testing
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