Bias variance decomposition for classifiers is a useful tool in understanding classifier behavior. Unfortunately, the literature does not provide consistent guidelines on how to apply a bias variance decomposition. This paper examines the various parameters and variants of empirical bias variance decompositions through an extensive simulation study. Based on this study, we recommend to use ten ...

Existing feature selection methods fail to properly account for interactions between features. In this paper, we attempt remedy issue by using orthogonal variance decomposition evaluate The orthogonality of the allows us directly calculate total contribution each output variance. As a result, obtain an efficient and technically sound algorithm which takes into interactions. proposed has low com...

Let e and Σ be respectively the vector of shocks and its variance covariance matrix in a linear system of equations in reduced form. This article shows that a unique orthogonal variance decomposition can be obtained if we impose a restriction that maximizes the trace of A, a positive definite matrix such that e Az = where z is vector of uncorrelated shocks with unit variance. Such a restriction...