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

We show that the byproducts of the standard training process of a randomforest yield not only the well known and almost computationally free out-of-bagpoint estimate of the model generalization error, but also give a direct pathto compute confidence intervals for the generalization error which avoidsprocesses of data splitting and model retraining. Besides the low computationalcost involved in their construction, these confidence intervals are shownthrough simulations to have good coverage and appropriate shrinking rate oftheir width in terms of the training sample size.

Confidence intervals for the random forest generalization error