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

Binary classification is a fundamental task in machine learning, withapplications spanning various scientific domains. Whether scientists areconducting fundamental research or refining practical applications, theytypically assess and rank classification techniques based on performancemetrics such as accuracy, sensitivity, and specificity. However, reportedperformance scores may not always serve as a reliable basis for researchranking. This can be attributed to undisclosed or unconventional practicesrelated to cross-validation, typographical errors, and other factors. In agiven experimental setup, with a specific number of positive and negative testitems, most performance scores can assume specific, interrelated values. Inthis paper, we introduce numerical techniques to assess the consistency ofreported performance scores and the assumed experimental setup. Importantly,the proposed approach does not rely on statistical inference but uses numericalmethods to identify inconsistencies with certainty. Through three differentapplications related to medicine, we demonstrate how the proposed techniquescan effectively detect inconsistencies, thereby safeguarding the integrity ofresearch fields. To benefit the scientific community, we have made theconsistency tests available in an open-source Python package.

Testing the Consistency of Performance Scores Reported for Binary Classification Problems