Bias Plus Variance Decomposition for Zero-One Loss Functions

Machine Learning : Proceedings of the Thirteenth International Conference , 1996 Bias Plus Variance Decomposition forZero - One Loss

We present a bias-variance decomposition of expected misclassiication rate, the most commonly used loss function in supervised classiication 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 ooered for the more commonly used zero-one (misclassiication) loss functions un...

A Unified Bias-Variance Decomposition and its Applications

This paper presents a unified bias-variance decomposition that is applicable to squared loss, zero-one loss, variable misclassification costs, and other loss functions. The unified decomposition sheds light on a number of significant issues: the relation between some of the previously-proposed decompositions for zero-one loss and the original one for squared loss, the relation between bias, var...

A Unified Bias-Variance Decomposition for Zero-One and Squared Loss

The bias-variance decomposition is a very useful and widely-used tool for understanding machine-learning algorithms. It was originally developed for squared loss. In recent years, several authors have proposed decompositions for zero-one loss, but each has significant shortcomings. In particular, all of these decompositions have only an intuitive relationship to the original squared-loss one. I...

Bias-Variance-Decomposition of Zero-One Loss in Average-Case Model

On Bias Plus Variance

This paper presents a Bayesian additive “correction” to the familiar quadratic loss biasplus-variance formula. It then discusses some other loss-function-specific aspects of supervised learning. It ends by presenting a version of the bias-plus-variance formula appropriate for log loss, and then the Bayesian additive correction to that formula. Both the quadratic loss and log loss correction ter...