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    Empirical comparison of tree ensemble variable importance measures

    Access Status
    Fulltext not available
    Authors
    Auret, L.
    Aldrich, Chris
    Date
    2011
    Type
    Journal Article
    
    Metadata
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    Citation
    Auret, Lidia and Aldrich, Chris. 2011. Empirical comparison of tree ensemble variable importance measures. Chemometrics and Intelligent Laboratory Systems. 105 (2): pp. 157-170.
    Source Title
    Chemometrics and Intelligent Laboratory Systems
    DOI
    10.1016/j.chemolab.2010.12.004
    ISSN
    0169-7439
    School
    WASM Minerals Engineering and Extractive Metallurgy Teaching Area
    URI
    http://hdl.handle.net/20.500.11937/47469
    Collection
    • Curtin Research Publications
    Abstract

    Tree ensembles are becoming well-established as popular and powerful data modelling techniques. Tree ensemble models are essentially black box models, although their individual members may not be, and with their growing popularity, interest in the interpretation of tree ensemble models has also grown. This study presents variable importance measures associated with random forests, conditional inference forests and boosted trees, and employs a number of simulated data sets to compare these methods. Overall, variable importance indicators based on bagged conditional inference forests appear to strike a good balance between identification of significant variables and avoiding unnecessary flagging of correlated variables. Data preprocessing and interpretation by experts knowledgeable with a specific data set remain vital.

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