Occam’s Razor
Explore the principle of Occam's Razor and its application in supervised machine learning. Understand how preferring simpler models with fewer parameters can reduce overfitting and improve generalization. Discover how this principle supports balancing bias and variance to enhance model performance on unseen data.
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In the previous lesson, we saw that overly complex models tend to overfit (high variance), whereas overly simple models tend to underfit (high bias). This trade-off raises a fundamental question: if two models perform equally well on unseen data, which one should we choose? The answer lies in the philosophical principle known as Occam’s Razor ...
What is Occam’s Razor?
Occam’s Razor is a problem-solving principle credited to the 14th-century English philosopher, William of Ockham.
It states that out of two or more competing theories, the simpler theory is to be preferred.
In the context of machine learning, this translates directly: a simple model should be preferred over a complex one, provided both achieve similar predictive performance (generalization). Choosing the simpler model offers practical benefits: it is easier to ...