Bias metric

Definition:

The average difference between predictions and the correct value for that observation

B(x) = L(ym, t)

ym: mean prediction

t: target vlaue

L : loss function

If our model is too simple and has very few parameters then it may have high bias and low variance. On the other hand if our model has large number of parameters then it’s going to have high variance and low bias. So we need to find the right/good balance without overfitting and underfitting the data. [src]

The bias-variance tradeoff is a central problem in supervised learning.

Ideally, one wants to choose a model that both accurately captures the regularities in its training data, but also generalizes well to unseen data. Unfortunately, it is typically impossible to do both simultaneously.

High-variance learning methods may be able to represent their training set well but are at risk of overfitting to noisy or unrepresentative training data.

In contrast, algorithms with high bias typically produce simpler models that don't tend to overfit but may underfit their training data, failing to capture important regularities.