bias-variance decomposition

(55 minutes to learn)


The bias-variance decomposition (often referred to as the bias-variance tradeoff) is a frequentist analysis of the generalization capability of an estimator, i.e. a learning algorithm.


This concept has the prerequisites:

Core resources (read/watch one of the following)


Supplemental resources (the following are optional, but you may find them useful)


Mathematical Monk: Machine Learning (2011)
Online videos on machine learning.
The Elements of Statistical Learning
A graudate-level statistical learning textbook with a focus on frequentist methods.
Authors: Trevor Hastie,Robert Tibshirani,Jerome Friedman


See also