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

Tree-based algorithms such as random forests and gradient boosted treescontinue to be among the most popular and powerful machine learning models usedacross multiple disciplines. The conventional wisdom of estimating the impactof a feature in tree based models is to measure the \textit{node-wise reductionof a loss function}, which (i) yields only global importance measures and (ii)is known to suffer from severe biases. Conditional feature contributions (CFCs)provide \textit{local}, case-by-case explanations of a prediction by followingthe decision path and attributing changes in the expected output of the modelto each feature along the path. However, Lundberg et al. pointed out apotential bias of CFCs which depends on the distance from the root of a tree.The by now immensely popular alternative, SHapley Additive exPlanation (SHAP)values appear to mitigate this bias but are computationally much moreexpensive. Here we contribute a thorough comparison of the explanationscomputed by both methods on a set of 164 publicly available classificationproblems in order to provide data-driven algorithm recommendations to currentresearchers. For random forests, we find extremely high similarities andcorrelations of both local and global SHAP values and CFC scores, leading tovery similar rankings and interpretations. Analogous conclusions hold for thefidelity of using global feature importance scores as a proxy for thepredictive power associated with each feature.

Data-driven advice for interpreting local and global model predictions in bioinformatics problems