\)` is some function that relates our `\(x\)` predictor variables to `\(y\)` in an unknown way thus we must estimate it. Epsilon `\(\epsilon\)` is the random error, is independent of `\(x\)` and averages to zero. Therefore, we can predict `\(y\)` using `\(\hat{y} = \hat{f}(x)\)` for new data (the [test dataset](https://en.wikipedia.org/wiki/Training,_validation,_and_test_sets#Test_dataset)) and evaluate how well the algorithm learned the target function when introduced to new data.  

**How to define machin