
Lucie Mougin developed end-to-end machine learning workflow capabilities for the blackSwanCS/Higgs_collaboration_B repository, focusing on reusable components and streamlined experimentation. She implemented a Boosted Decision Tree training notebook and introduced a loader for pretrained models, enabling seamless integration and prediction with existing assets. Using Python, Lucie enhanced model training by exposing XGBoost hyperparameters and added a significance curve visualization to support model validation and evaluation. Her work emphasized data preprocessing, model deployment, and structured asset management, laying the groundwork for scalable experimentation. Over the month, she delivered two features that improved both the technical depth and future extensibility of the project.

June 2025 monthly summary for Higgs_collaboration_B (blackSwanCS). Focused on delivering end-to-end ML workflow capabilities and enhanced model training tools, with emphasis on business value through reusable components and faster experimentation cycles.
June 2025 monthly summary for Higgs_collaboration_B (blackSwanCS). Focused on delivering end-to-end ML workflow capabilities and enhanced model training tools, with emphasis on business value through reusable components and faster experimentation cycles.
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