 Intuitively, the bias-variance decomposition brings to light a trade-off between two extreme approaches to making a prediction. At one extreme, you might adopt as an estimator a constant function which produces the same answer no matter what data you see. Suppose 7 is your lucky number and your estimator’s prediction, \(h(X) = 7\). Then the variance of \(h(\cdot)\) would be zero, since its prediction is always the same. The bias of your estimator, however, will be very large. In other words, your lucky number 7 model will massively under fit your data.