Paper ID: 2209.09283

Machine Learning Class Numbers of Real Quadratic Fields

Malik Amir, Yang-Hui He, Kyu-Hwan Lee, Thomas Oliver, Eldar Sultanow

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.

Submitted: Sep 19, 2022