Google

The definitions of bias and variance used here are chosen to convey insight on how to improve a learning algorithm and differ from how statisticians define bias and variance. Technically, what is defined here as Bias should be called Error we attribute to bias, and Avoidable bias should be called error attributed to the learning algorithm's bias that is over the optimal error rate.

Informal Bias and Variance Definitions Differ from Statisticians' Definitions

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

Why does Andrew Ng choose informal definitions of bias and variance?

Do Andrew Ng's definitions of bias and variance match statisticians' definitions?

Technical name for defined Bias is _____ to bias.

Match informal terms with their technical statistical meanings.

Order the reasoning steps to identify technical avoidable bias.

What is the technical definition of Avoidable Bias?

Is the technical term for Bias 'Error we attribute to bias'?

Avoidable bias is error attributed to the algorithm's bias over _____ error rate.

Match terms to their distinct definition types.

Order the decision steps when choosing definitions for bias and variance.

Learn Before

Related