A new variable importance measure for random forests with missing data

By Alexander Hapfelmeier, Torsten Hothorn, Kurt Ulm, Carolin Strobl. 2012

201 citations recorded by the source corpus.

Subfields: Environmental Engineering, Nature and Landscape Conservation, Statistics and Probability

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Citations in the corpus

All Models are Wrong, but Many are Useful: Learning a Variable’s Importance by Studying an Entire Class of Prediction Models Simultaneously (2018, methodology): “, 2016), and several variations of the measure have been proposed (Strobl et al., 2007, 2008; Altmann et al., 2010; Hapfelmeier et al., 2014).”
Interpretable Machine Learning - A Brief History, State-of-the-Art and Challenges (2020, background): “2 The random forest paper has been cited over 60,000 times (Google Scholar; September 2020) and there are many papers improving the importance measure ([110,111,44,56]) which are also cited frequently.”
A Brief Review of Random Forests for Water Scientists and Practitioners and Their Recent History in Water Resources (2019, background): “[76], and for cases with high-dimensional data (i.”
The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “…approaches are typically restricted to importance in fitting data, they do have the advantage of also extending well to classification models, for example, using random forests (e.g., Hapfelmeier et al., 2014), allowing for sensitivity measures that apply to both discrete and continuous variables.”
A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding. (2015, background): “The following advantages of random forests have been reported in the literature [33,126–136], including its ability to: deal with large datasets without variable deletion; provide a feature importance measure of the predictor variables (mean decrease in accuracy); a measure of the internal structure”
All Models are Wrong but many are Useful: Variable Importance for Black-Box, Proprietary, or Misspecified Prediction Models, using Model Class Reliance (2018, methodology): “, 2016), and several variations of the measure have been proposed (Strobl et al., 2007, 2008; Altmann et al., 2010; Hapfelmeier et al., 2014).”