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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Understanding the nature of the errors of a machine learning system is often difficult for multiclass classification problems with a large number of classes. This is true even more so if the number of examples for each class is low. To interpret the performance of a multiclass classifier, we form a graph representing the errors, and use average-link clustering to find groups of classes which are confused with each other. We apply this idea to the QANTA question answering system (Iyyer et al., 2014), and provide a method of analysis of the clusters.

Using Confusion Graphs to Understand Classifier Error