Back to Machine Learning Basics - Regularization

During the training process, we calculate how well the model performs and modify parameters of the f(X) so our result is closer to the real values of Y. While we are doing that we calculate the error of our model. We put in the sample, calculate the error based on the real Y value and modify the parameters of the f(X). The error produced this way is called reducible error because it can be minimized and even completely removed (not a good idea btw). The other part of the equation from above is e – irreducible error.