How to Calculate the Bias-Variance Trade-off with Python

A model with high variance will change a lot with small changes to the training dataset. Conversely, a model with low variance will change little with small or even large changes to the training dataset. The variance is always positive. On the whole, the error of a model consists of reducible error and irreducible error. The reducible error is the element that we can improve. It is the quantity that we reduce when the model is learning on a training dataset and we try to get this number as close to zero as possible. The irreducible error is the error that we can not remove with our model, or with any model. The error is caused by elements outside our control, such as statistical noise in the observations.