Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models

DOI10.1080/10618600.2021.2007935OpenAlexW3217755000MaRDI QIDQ5057087

C. Hurley, Andrew C. Parnell, Unnamed Author

Publication date: 15 December 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2108.04310

zbMATH Keywords

black-boxmodel explanationmodel visualization

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models ⋮ vivid

Uses Software

• R
• igraph
• randomSurvivalForest
• seriation
• PairViz
• ElemStatLearn
• zenplots

Cites Work

• Predictive learning via rule ensembles
• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously
• Multivariate adaptive regression splines
• Interpretation of interaction: a review
• Eulerian tour algorithms for data visualization and the \({{\mathtt PairViz}}\) package
• Visualizing Variable Importance and Variable Interaction Effects in Machine Learning Models
• Visualizing the Effects of Predictor Variables in Black Box Supervised Learning Models
• High-Dimensional Variable Selection for Survival Data
• Random forests

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