# 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 *F(X)*. *Variance* is given by . It is a measure of extent to which the solution for a given dataset varies around the mean over all datasets. Hence, *Variance* is a measure of how much the function is sensitive to the particular choice of dataset *D*. The third term *Noise*, as mentioned earlier, is the expectation of difference between observation and the true regression function, over all the values of *X* and *Y*. Putting all these together, we can write the following:

The objective of machine learning is to learn the function from data that minimizes...