A new variable importance measure for random forests with missing data

Volume: 24, Issue: 1, Pages: 21 - 34
Published: Jan 1, 2014
Abstract
Random forests are widely used in many research fields for prediction and interpretation purposes. Their popularity is rooted in several appealing characteristics, such as their ability to deal with high dimensional data, complex interactions and correlations between variables. Another important feature is that random forests provide variable importance measures that can be used to identify the most important predictor variables. Though there...
Paper Details
Title
A new variable importance measure for random forests with missing data
Published Date
Jan 1, 2014
Volume
24
Issue
1
Pages
21 - 34
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