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humans have a hard time solving (e.g., predicting what movie to recommend, or what ad to
show to a user) it can be hard to estimate the optimal error rate.

In the section “Comparing to Human-Level Performance (Chapters 33 to 35), I will discuss
in more detail the process of comparing a learning algorithm’s performance to human-level
performance.

In the last few chapters, you learned how to estimate avoidable/unavoidable bias and

variance by looking at training and dev set error rates. The next chapter will discuss how you
can use insights from such an analysis to prioritize techniques that reduce bias vs.
techniques that reduce variance. There are very different techniques that you should apply
depending on whether your project’s current problem is high (avoidable) bias or high
variance. Read on!

Page 48 Machine Learning Yearning-Draft Andrew Ng