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

One of the key elements of explanatory analysis of a predictive model is toassess the importance of individual variables. Rapid development of the area ofpredictive model exploration (also called explainable artificial intelligenceor interpretable machine learning) has led to the popularization of methods forlocal (instance level) and global (dataset level) methods, such asPermutational Variable Importance, Shapley Values (SHAP), Local InterpretableModel Explanations (LIME), Break Down and so on. However, these methods do notuse information about the correlation between features which significantlyreduce the explainability of the model behaviour. In this work, we propose newmethods to support model analysis by exploiting the information about thecorrelation between variables. The dataset level aspect importance measure isinspired by the block permutations procedure, while the instance level aspectimportance measure is inspired by the LIME method. We show how to analyzegroups of variables (aspects) both when they are proposed by the user and whenthey should be determined automatically based on the hierarchical structure ofcorrelations between variables. Additionally, we present the new type of modelvisualisation, triplot, which exploits a hierarchical structure of variablegrouping to produce a high information density model visualisation. Thisvisualisation provides a consistent illustration for either local or globalmodel and data exploration. We also show an example of real-world data with 5kinstances and 37 features in which a significant correlation between variablesaffects the interpretation of the effect of variable importance. The proposedmethod is, to our knowledge, the first to allow direct use of the correlationbetween variables in exploratory model analysis.

Triplot: model agnostic measures and visualisations for variable  importance in predictive models that take into account the hierarchical  correlation structure