Biased Model

A Biased Model is a model that systematically differs from its referent.

See: Cognitive Bias, Statistical Bias, Biased Estimator, Inductive Bias, Bisased Statistic; Prejudice; Biased Frame of Reference; Nudge; Marketing Task.

References

(Sammut & Webb, 2011) ⇒ Claude Sammut (editor), and Geoffrey I. Webb (editor). (2011). “Bias.” In: (Sammut & Webb, 2011) p.97
- QUOTE: Bias has two meanings, inductive bias, and statistical bias see bias variance decomposition.

http://en.wiktionary.org/wiki/bias#Noun
1. Template:Countable Template:Uncountable inclination towards something; predisposition, partiality, prejudice, preference, predilection.
2. Template:Countable (textile) the diagonal line between warp and weft in a woven fabric.
3. Template:Electronics a voltage or current applied for example to a transistor electrode
4. Template:Statistics the difference between the expectation of the sample estimator and the true population value, which reduces the representativeness of the estimator by systematically distorting it
5. Template:Sports In the game of crown green bowls: a weight added to one side of a bowl so that as it rolls, it will follow a curved rather than a straight path; the oblique line followed by such a bowl; the lopsided shape or structure of such a bowl.
http://wordnetweb.princeton.edu/perl/webwn?s=bias
- prejudice, preconception (a partiality that prevents objective consideration of an issue or situation)

http://en.wikipedia.org/wiki/Bias_of_an_estimator
- In Statistics, bias (or bias function) of an Estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. Otherwise the estimator is said to be biased.
  In ordinary English, the term bias is pejorative. In statistics, there are problems for which it may be good to use an estimator with a small bias. In some cases, an estimator with a small bias may have lesser mean squared error or be median-unbiased (rather than mean-unbiased, the standard unbiasedness property). The property of median-unbiasedness is invariant under transformations while the property of mean-unbiasedness may be lost under nonlinear transformations.

(Kohavi & Wolpert, 1996) ⇒ Ron Kohavi, and David Wolpert. (1996). “Bias Plus Variance Decomposition for Zero-One Loss Functions.” In: Proceedings of the 13th International Conference on Machine Learning (ICML 1996).
- QUOTE: We present a bias-variance decomposition of expected misclassication rate, the most commonly used loss function in supervised classication learning. The bias-variance decomposition for quadratic loss functions is well known and serves as an important tool for analyzing learning algorithms, yet no decomposition was offered for the more commonly used zero-one (misclassication)loss functions until the recent work of Kong & Dietterich (1995) and Breiman (1996). … We show that, in practice, the naive frequency-based estimation of the decomposition terms is by itself biased and show how to correct for this bias …