English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Although the methods of bagging and random forests are some of the mostwidely used prediction methods, relatively little is known about theiralgorithmic convergence. In particular, there are not many theoreticalguarantees for deciding when an ensemble is "large enough" --- so that itsaccuracy is close to that of an ideal infinite ensemble. Due to the fact thatbagging and random forests are randomized algorithms, the choice of ensemblesize is closely related to the notion of "algorithmic variance" (i.e. thevariance of prediction error due only to the training algorithm). In thepresent work, we propose a bootstrap method to estimate this variance forbagging, random forests, and related methods in the context of classification.To be specific, suppose the training dataset is fixed, and let the randomvariable $Err_t$ denote the prediction error of a randomized ensemble of size$t$. Working under a "first-order model" for randomized ensembles, we provethat the centered law of $Err_t$ can be consistently approximated via theproposed method as $t\to\infty$. Meanwhile, the computational cost of themethod is quite modest, by virtue of an extrapolation technique. As aconsequence, the method offers a practical guideline for deciding when thealgorithmic fluctuations of $Err_t$ are negligible.

Estimating the Algorithmic Variance of Randomized Ensembles via the Bootstrap