Machine learning from data: more samples, better accuracy

Computer ScienceData, AI & Machine LearningAges 16–17

A simple classifier learns from labeled samples: it computes the centroid of each class, then uses the perpendicular bisector between the two centroids as the decision boundary. Students adjust the number of training samples, the label-noise rate and how skewed the data collection is, the model retrains, and its accuracy is measured on a fixed set of 400 test samples. When training data comes mostly from one group, overall accuracy still looks high, but accuracy for the underrepresented group drops sharply — that is data bias in action.