Intervention in prediction measure: a new approach to assessing variable importance for random forests
Abstract
Random forests are a popular method in many fields since they can be successfully applied to complex data, with a small sample size, complex interactions and correlations, mixed type predictors, etc. Furthermore, they provide variable importance measures that aid qualitative interpretation and also the selection of relevant predictors. However, most of these measures rely on the choice of a performance measure. But measures of prediction...
Paper Details
Title
Intervention in prediction measure: a new approach to assessing variable importance for random forests
Published Date
May 2, 2017
Journal
Volume
18
Issue
1
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