If You Can't Explain It to a Six-Year-Old, You Don't Understand It

If You Can't Explain It to a Six-Year-Old, You Don't Understand It "If you can't explain it to a six-year-old, you don't understand it yourself." — Attributed to Albert Einstein Every machine learning model faces one fundamental dilemma: it needs to learn general patterns from data, not memorize the data itself. Memorization is called overfitting — and regularization is the umbrella term for all the tricks we use to prevent it. Think of a student who studies by reading one textbook over and over until they memorize every sentence. When exam day comes with slightly different wording, they fall apart. A well-regularized model is the student who truly understands the material — they can handle anything the exam throws at them. 🔴 01 — L1 Regularization (Lasso) L1 regularization adds a penalty equal to the sum of the absolute values of all model weights to the loss function. This encourages the model to drive unimportant weights all the way to zero — effectively removing features. The Formu