Class Based Variable Importance for Medical Decision Making by Danielle Baghernejad in BJSTR
Abstract
In this paper we explore variable importance within tree-based
modeling, discussing its strengths and weaknesses with regard to medical
inference and action ability. While variable importance is useful in
understanding how strongly a variable influences a tree, it does not
convey
how variables relate to different classes of the target variable. Given
that in the medical setting, both prediction and inference are important
for
successful machine learning, a new measure capturing variable importance
with regards to classes is essential. A measure calculated from the
paths of training instances through the tree is defined, and initial
performance on benchmark datasets is explored.
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