Final answer:
Occam's razor, attributed to William of Ockham, advises choosing the simplest explanation with the fewest assumptions. In statistical learning, it is applied by selecting simpler models to avoid overfitting, thus balancing the bias-variance trade-off.
Step-by-step explanation:
Occam's razor is a philosophical principle that suggests the simplest explanation is often the best one. Formulated by the theologian William of Ockham in the 13th century, it advises that "entities should not be multiplied beyond necessity." In other words, when faced with competing hypotheses or explanations that make the same predictions, the one with the fewest assumptions should be selected.
In the context of statistical learning, Occam's razor can be practiced by choosing models that are simpler and have fewer parameters, assuming they perform comparably to more complex models. This often leads to models that are easier to interpret and are less prone to overfitting. Overfitting, a situation where a model captures noise rather than the underlying process, illustrates a trade-off between bias and variance in machine learning. Simpler models tend to have higher bias but lower variance, while complex models often exhibit low bias but high variance. Applying Occam's razor aims to balance this trade-off to achieve better generalization on unseen data.