What is overfitting?

- [Instructor] In this lesson we're going to talk about … another concept that we've briefly mentioned previously. … This problem is on the opposite extreme of underfitting … and not surprisingly this is called overfitting. … Once again, recall this plot and equation … from the last two lessons. … Total error is the sum of bias, variance … and some irreducible error that we can't control. … We learned in the last lesson … that the left side of this plot represents underfitting. … On the other extreme the right side … of this plot represents overfitting. … So we're looking at a very complex model … where we have low bias, but high variance, … which drives high total error. … Revisiting prior definitions, variance refers … to an algorithm's sensitivity to small fluctuations … in the training data. … High variance is a result of an algorithm fitting … to random noise in the training data. … And overfitting occurs when an algorithm … fits too closely to a limited set of data. … In other words, the model might just memorize the examples …