%0 Journal Article
%T On Mean Squared Error of Hierarchical Estimator
%A Brodowski, Stanisław
%J Schedae Informaticae
%V 2011
%R 10.4467/20838476SI.11.004.0290
%N Volume 20
%P 83-99
%K Hierarchial Estimator, hierarchical model, regression, function approximation, error, theorem
%@ 1732-3916
%D 2012
%U https://ejournals.eu/en/journal/schedae-informaticae/article/on-mean-squared-error-of-hierarchical-estimator
%X 
	In this paper a new theorem about components of the mean squared error of Hierarchical Estimator is presented. Hierarchical Estimator is a machine learning meta-algorithm that attempts to build, in an incremental and hierarchical manner, a tree of relatively simple function estimators and combine their results to achieve better accuracy than any of the individual ones. The components of the error of a node of such a tree are: weighted mean of the error of the estimator in a node and the errors of children, a non-positive term that descreases below 0 if children responses on any example dier and a term representing relative quality of an internal weighting function, which can be conservatively kept at 0 if needed. Guidelines for achieving good results based on the theorem are brie discussed.