Biased Model

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





    • 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 …