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Some changes to a learning algorithm can address the first component of error—​bias​—and
improve its performance on the training set. Some changes address the second
component—​variance​—and help it generalize better from the training set to the dev/test
7
sets. To select the most promising changes, it is incredibly useful to understand which of
these two components of error is more pressing to address.

Developing good intuition about Bias and Variance will help you choose effective changes for

your algorithm.

this setting. When your error metric is mean squared error, you can write down formulas specifying
these two quantities, and prove that Total Error = Bias + Variance. But for our purposes of deciding
how to make progress on an ML problem, the more informal definition of bias and variance given
here will suffice.

7 There are also some methods that can simultaneously reduce bias and variance, by making major
changes to the system architecture. But these tend to be harder to identify and implement.

Page 43 Machine Learning Yearning-Draft Andrew Ng