# Bias-Variance Tradeoff

(Redirected from bias-variance tradeoff)

A Bias-Variance Tradeoff is a tradeoff between prediction bias and prediction variance.

**See:**Errors And Residuals in Statistics, Supervised Learning, Estimator, Overfitting, Expected Value, Bias, Variance.

## References

### 2017

- (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Bias–variance_tradeoff Retrieved:2017-11-25.
- In statistics and machine learning, the
**bias–variance tradeoff**(or**dilemma**) is the problem of simultaneously minimizing two sources of error that prevent supervised learning algorithms from generalizing beyond their training set:* The*bias*is error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting).- The
*variance*is error from sensitivity to small fluctuations in the training set. High variance can cause an algorithm to model the random noise in the training data, rather than the intended outputs (overfitting).

- The
- The
**bias–variance decomposition**is a way of analyzing a learning algorithm's expected generalization error with respect to a particular problem as a sum of three terms, the bias, variance, and a quantity called the*irreducible error*, resulting from noise in the problem itself.This tradeoff applies to all forms of supervised learning: classification, regression (function fitting), and structured output learning. It has also been invoked to explain the effectiveness of heuristics in human learning.

- In statistics and machine learning, the

### 2011

- (Rajnarayan & Wolpert, 2011) ⇒ Dev Rajnarayan, and David Wolpert. (2011). “Bias-Variance Trade-offs; Novel Applications.” In: (Sammut & Webb, 2011) p.101

### 2004

- (Bouchard & Triggs, 2004) ⇒ Guillaume Bouchard, and Bill Triggs. (2004). “The Trade-off Between Generative and Discriminative Classifiers.” In: Proceedings of COMPSTAT 2004.
- QUOTE: … The key argument is that the discriminative estimator converges to the conditional density that minimizes the negative log-likelihood classification loss against the true density p(x, y) [2]. For finite sample sizes, there is a bias-variance tradeoff and it is less obvious how to choose between generative and discriminative classifiers.

### 1996

- (Kohavi & Wolpert, 1996) ⇒ Ron Kohavi, and David H. Wolpert. (1996). “Bias Plus Variance Decomposition for Zero-One Loss Functions.” In: Proceedings of the 13th International Conference on Machine Learning (ICML 1996).