Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Understanding Global Feature Contributions With Additive Importance
  Measures   Understanding the inner workings of complex machine learning models is a
long-standing problem and most recent research has focused on local
interpretability. To assess the role of individual input features in a global
sense, we explore the perspective of defining feature importance through the
predictive power associated with each feature. We introduce two notions of
predictive power (model-based and universal) and formalize this approach with a
framework of additive importance measures, which unifies numerous methods in
the literature. We then propose SAGE, a model-agnostic method that quantifies
predictive power while accounting for feature interactions. Our experiments
show that SAGE can be calculated efficiently and that it assigns more accurate
importance values than other methods.

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The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
27
The categories of this paper are: comp-gas
22
The categories of this paper are: chao-dyn
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The categories of this paper are: acc-phys
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The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
25
The categories of this paper are: patt-sol
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph