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

Models which estimate main effects of individual variables alongsideinteraction effects have an identifiability challenge: effects can be freelymoved between main effects and interaction effects without changing the modelprediction. This is a critical problem for interpretability because it permits"contradictory" models to represent the same function. To solve this problem,we propose pure interaction effects: variance in the outcome which cannot berepresented by any smaller subset of features. This definition has anequivalence with the Functional ANOVA decomposition. To compute thisdecomposition, we present a fast, exact algorithm that transforms anypiecewise-constant function (such as a tree-based model) into a purified,canonical representation. We apply this algorithm to Generalized AdditiveModels with interactions trained on several datasets and show large disparity,including contradictions, between the effects before and after purification.These results underscore the need to specify data distributions and ensureidentifiability before interpreting model parameters.

Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models