Bias and Variance

Variance and Bias are two critical concepts in machine learning and statistics, that describe the behavior of models, to determine how well a model can make predictions on unseen data that was not used to train the model.

Variance and Bias are errors where the goal is to find a balance between these two types of errors to create a machine-learning model that generalizes well to new and unseen data while still managing to capture the underlying patterns. This trade-off is often referred to as the bias-variance trade-off.

Understanding Bias

Imagine you’re an archer aiming to hit a target with your arrows. The target represents the true values or actual outcomes you want your model to predict. Your goal is to get as close to the bullseye (the center of the target) as possible.

Low Bias (Good)

If you are playing archery and your arrows consistently hit very close to the bullseye, you have low bias. In this case, you’re accurate, and your predictions are close to the actual values.

Low bias means that a model has the capacity to capture and fit the underlying patterns in the data accurately.