Publications

(Version 1.00 – January 15 th , 2026) Mr. Jorge RODRIGUEZ When data scientists build a model, the first stop is usually the evaluation metric mean squared error, log-loss, F1 score, and the like. We track performance across training, validation, and test sets to check that the model isn’t just memorizing patterns but can actually generalize. On paper, that’s what makes a model look “ready” for the real world. But the world outside of datasets is messy and that’s where the gap

(Version 1.18 – January 07, 2026) Ms. Yumi HEO What is multilabel classification? Imagine classifying the colour of a single skin mole not just as light brown or dark brown but as both light brown and dark brown simultaneously. This is the core task of multilabel classification: training a machine learning model to assign multiple relevant labels to a single input. However, accurately evaluating this model often encounters a greater challenge than evaluating a standard classi