Does nutrition feel too complicated? You start with a simple rule: mostly whole foods, enough protein, not too much junk. Then you keep adding just one more constraint until eating feels like a full-time job. This can be explained using a machine learning concept called overfitting.
What’s Overfitting?
Overfitting occurs when a machine learning model fits the training data too closely and fails to capture general patterns. It memorizes random noise, so while performance on training data looks great, results on new data suffer. Excessive flexibility makes the model fragile.
This is similar to trying to follow nutrition rules from every paper and podcast: strict meal timing, no seed oils, no artificial sweeteners, no gluten, no nightshades, no plastic contact, only certain cooking methods. Your model explains every past health worry you’ve ever had; it fits your history of symptoms, anecdotes, and fears, but it’s so hyper‑specific the smallest deviation feels like a failure. We can test for overfitting using a technique called cross-validation.
Cross-Validation
In statistics, cross-validation evaluates how well a model generalizes. You don’t train on all the data at once. Instead, you split the data into parts. You train on some and test on the rest, rotating which part to hold out. The idea is to estimate how the model behaves on data it hasn’t seen.
Similarly, for nutrition, you can treat phases of your life as different “folds.” Ask yourself: Would these rules have worked during busy weeks or travel, or do they only work in ideal conditions? If your rule set fails outside a narrow window, it’s not robust. Good guidelines should survive life’s ongoing changes. Recognizing overfitting is one thing, but to resolve it we turn to another technique called regularization.
Regularization
Regularization in machine learning means you don’t just reduce error; you also penalize complexity. Mathematically, you add a term to the objective that grows when the model uses many large coefficients. That nudges the model toward simpler explanations that still fit well but are less sensitive to noise.
Lasso is a specific form of regularization that penalizes coefficients (or variables) and often forces some to be exactly zero. Instead of “a little bit of everything,” the model keeps only a subset of variables. It discards the rest. It simplifies and selects simultaneously.
Likewise, in our example, regularization means making extra dietary rules expensive. Any additional rule (no seed oils, fasting, specific supplement) has to prove its worth in complexity. If a new constraint doesn’t clearly improve your health outcomes or sanity, it gets removed. You give up a bit of satisfying precision in exchange for simplicity, letting you focus on other areas of your life.