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

Have you ever wondered how your feature space is impacting the prediction ofa specific sample in your dataset? In this paper, we introduce Single SampleFeature Importance (SSFI), which is an interpretable feature importancealgorithm that allows for the identification of the most important featuresthat contribute to the prediction of a single sample. When a dataset can belearned by a Random Forest classifier or regressor, SSFI shows how the RandomForest's prediction path can be utilized for low-level feature importancecalculation. SSFI results in a relative ranking of features, highlighting thosewith the greatest impact on a data point's prediction. We demonstrate theseresults both numerically and visually on four different datasets.

Single Sample Feature Importance: An Interpretable Algorithm for Low-Level Feature Analysis