It is fair to say that many of the prominent examples bias in Machine Learning (ML) arise from training data. In fact, some would argue supervised ML algorithms cannot be biased, they reflect data on which are trained. this paper, we demonstrate how can misrepresent through underestimation. We show irreducible error, regularization, and feature class imbalance contribute The paper concludes wit...