Occam learning

Occam learning, often associated with the principle of Occam's Razor, refers to a concept in machine learning and statistical modeling that suggests choosing the simplest model among competing hypotheses that adequately explains the data. The idea is based on the philosophical principle attributed to William of Ockham, which states that one should not multiply entities beyond necessity; in a scientific context, it implies that the simplest explanation is often the best.