Data Science, Regularization, Machine Learning, Editors Pick, Hands On Tutorials.
balance the two sources of error a model can have: Bias relates to the training error, i.e., the error from predictions on the training set. Variance relates to the generalization error, the error from predictions on the test set. This linear model has a relatively high variance. Let’s use