During April 2026, Black Ej focused on enhancing model evaluation reliability in the scikit-learn/scikit-learn repository. He addressed a critical issue in the Calibration Curve computation by refining the bin-counting logic, specifically updating the minimum bin length calculation to use the intended number of bins. This Python-based fix improved the accuracy of probability calibration, directly impacting the reliability of probabilistic classifiers and downstream metrics such as reliability diagrams and the Brier score. His work demonstrated a solid understanding of data analysis and machine learning, and involved effective open-source collaboration through co-authored commits, reflecting depth in both technical and collaborative skills.