Machine Learning For Absolute Beginners: A Plain English Introduction (Second Edition) (Learn AI & Python for Beginners)

by Oliver Theobald

Bias refers to the gap between the value predicted by your model and the actual value of the data. In the case of high bias, your predictions are likely to be skewed in a particular direction away from the actual values. Variance describes how scattered your predicted values are in relation to each other.

The more the predictions deviate from the bull’s-eye, the higher the bias and the less reliable your model is at making accurate predictions.

it’s the prediction error that you want to minimize, not the bias or variance specifically.