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

Practitioners building classifiers often start with a smaller pilot datasetand plan to grow to larger data in the near future. Such projects need atoolkit for extrapolating how much classifier accuracy may improve from a 2x,10x, or 50x increase in data size. While existing work has focused on finding asingle "best-fit" curve using various functional forms like power laws, weargue that modeling and assessing the uncertainty of predictions is criticalyet has seen less attention. In this paper, we propose a Gaussian process modelto obtain probabilistic extrapolations of accuracy or similar performancemetrics as dataset size increases. We evaluate our approach in terms of error,likelihood, and coverage across six datasets. Though we focus on medical tasksand image modalities, our open source approach generalizes to any kind ofclassifier.

A Probabilistic Method to Predict Classifier Accuracy on Larger Datasets given Small Pilot Data