Variable Importance Assessment in Regression: Linear Regression versus Random Forest

By Ulrike Grömping. 2009

399 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/57bee7cb259f26414f02b7625aae1e8ed3cae237

Citations in the corpus

• Nonparametric variable importance assessment using machine learning techniques (2020, neutral): “The former category includes, for example, variable importance measures for random forests (Breiman, 2001; Ishwaran, 2007; Strobl et al. , 2007; Grömping, 2009) and neural networks (see, eg, Olden et al. , 2004), and ANOVA in linear models.”
• Selecting critical features for data classification based on machine learning methods (2020, neutral): “Hence, Grömping [17] compares the two approaches (linear model and random forest) and finds both striking similarities and differences, some of which can be explained whereas others remain a challenge.”