Finding the optimal tradeoff

- [Narrator] In the last few lessons,…we've talked about bias, variance,…underfitting, and overfitting.…In this lesson,…we're going to put all of these concepts together…to try to explain what it means…to find the optimal trade-off to minimize that total error.…So let's revisit this plot just one more time.…It highlights that underfitting is on the left,…overfitting is on the right,…and it calls out that there are…some optimal model complexity right in the middle…where we have an ideal trade-off between bias and variance…to achieve minimum total error.…

That's what we're trying to find.…Now let's look at this in a slightly different way.…Just looking at a scale of complexity,…we have this very simple model.…The simple model will underfit our data…and won't learn the true pattern,…and based on the plot in the prior slide,…we know that this means high bias and low variance.…Now on the other extreme,…we have an overly complex model.…This model is overfitting…and essentially just memorizing the training set,…and we know this means low bias and high variance.…