A new variable importance measure for random forests with missing data

DOI10.1007/s11222-012-9349-1zbMath1325.62011OpenAlexW2017665047MaRDI QIDQ892440

Kurt Ulm, Alexander Hapfelmeier, Carolin Strobl, Torsten Hothorn

Publication date: 19 November 2015

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-012-9349-1

zbMATH Keywords

missing datarandom forestsmissing valuesvariable importance measurespermutation importance

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Variable selection by random forests using data with missing values, Interaction forests: identifying and exploiting interpretable quantitative and qualitative interaction effects, An update on statistical boosting in biomedicine, Ordinal trees and random forests: score-free recursive partitioning and improved ensembles, Efficient permutation testing of variable importance measures by the example of random forests, All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously, Predictive Distribution Modeling Using Transformation Forests, Sparsity Oriented Importance Learning for High-Dimensional Linear Regression

Uses Software

• R
• UCI-ml
• party
• GeneSrF
• MICE
• C4.5

Cites Work

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• Bagging predictors
• Recursive partitioning on incomplete data using surrogate decisions and multiple imputation
• Unbiased split selection for classification trees based on the Gini index
• Empirical characterization of random forest variable importance measures
• Bias in information-based measures in decision tree induction
• Fully conditional specification in multivariate imputation
• Inference and missing data
• Random Forests and Adaptive Nearest Neighbors
• Random forests