Bias and Variance Tradeoff Explained

Every machine learning model makes two kinds of mistakes, and understanding them helps explain why some models perform poorly, even after training.

Imagine a student preparing for an exam. If they barely study, they get most answers wrong, no matter the question. This is similar to bias, where a model is too simple to capture the real pattern in data.

Now imagine a different student who memorizes the exact practice questions word for word. They score perfectly on those, but fail when the real exam has different wording. This is similar to variance, where a model fits the training data too closely and struggles with new data.

Understanding the tradeoff

• High bias leads to underfitting, where the model is too simple to learn the real pattern.
• High variance leads to overfitting, where the model learns the training data too precisely, including noise.
• The goal is to find a balance: the model is complex enough to learn patterns but simple enough to generalize well.

Real-world example

A house price model that always predicts the same average price, regardless of size or location, has high bias. A model that predicts prices perfectly for training houses, but wildly wrong for new houses, has high variance. The best model sits between these two extremes.