Learning Bayesian Models with R

Model overfitting and bias-variance tradeoff

The expected loss mentioned in the previous section can be written as a sum of three terms in the case of linear regression using squared loss function, as follows:

Here, Bias is the difference between the true model F(X) and average value of taken over an ensemble of datasets. Bias is a measure of how much the average prediction over all datasets in the ensemble differs from the true regression function ...