  We implement and interpret various supervised learning experiments involving
real quadratic fields with class numbers 1, 2 and 3. We quantify the relative
difficulties in separating class numbers of matching/different parity from a
data-scientific perspective, apply the methodology of feature analysis and
principal component analysis, and use symbolic classification to develop
machine-learned formulas for class numbers 1, 2 and 3 that apply to our
dataset.
