Occam’s Razor is a principle that suggests that the simplest explanation or solution, often the one that requires the fewest assumptions, is usually the best one. It is widely used in machine learning to maintain model simplicity and prevent overfitting.
Imagine you hear a noise in the next room. You might think it was made by a mouse, a cat, a dog, or even a ghost. But if you see a mouse in that room, you would likely think the mouse made the noise, right? Occam’s Razor is the idea that the simplest explanation is often the best one. Instead of thinking about ghosts, we pick the simplest thing - the mouse.
Occam’s Razor is a problem-solving principle coined by the British logician and Franciscan friar, William of Ockham. In AI and machine learning, it instructs us to prefer simpler models over more complex ones when they perform comparably well. Simplicity here doesn’t mean a model is easy to understand but usually points towards models with fewer parameters, which often translates to smaller computational cost and better generalization.
One of the main reasons to prefer simpler models is to avoid overfitting. Overfitting happens when a model learns too much from the training data, including the noise and outliers, and fails to generalize well to unseen data. By using Occam’s Razor to guide model selection, we can prevent overfitting.
A practical example could be choosing between a complex neural network or a linear regression model. If both models generate similar results, but the neural network has many more parameters, Occam’s Razor supports selecting the linear regression model.
Öccam’s Razor isn’t a rule, however. There are cases, mainly when dealing with large and intricate datasets, where more complicated models outperform their simpler counterparts and are therefore necessary. It acts more as a sanity check, encouraging not to make things more complicated than they need to be.
The principle relates to bias-variance tradeoff: bias is the error you make by approximating a real-life problem, which is complex, by a much simpler model. Variance is the amount by which your model would change if you estimated it using a different training dataset. Simpler models often have high bias but low variance, whereas more complex models have high variance but low bias. Occam’s Razor implicitly avoids high variance models especially when the decrement in bias is negligible.
Overfitting, Underfitting, Model Complexity, Bias-Variance Tradeoff, Regularization