Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accurate Shapley Values for explaining tree-based models   Although Shapley Values (SV) are widely used in explainable AI, they can be
poorly understood and estimated, implying that their analysis may lead to
spurious inferences and explanations. As a starting point, we remind an
invariance principle for SV and derive the correct approach for computing the
SV of categorical variables that are particularly sensitive to the encoding
used. In the case of tree-based models, we introduce two estimators of Shapley
Values that exploit the tree structure efficiently and are more accurate than
state-of-the-art methods. Simulations and comparisons are performed with
state-of-the-art algorithms and show the practical gain of our approach.
Finally, we discuss the ability of SV to provide reliable local explanations.
We also provide a Python package that computes our estimators at
https://github.com/salimamoukou/acv00.

5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
15
The categories of this paper are: nucl-ex
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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: alg-geom
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The categories of this paper are: econ
11
The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: dg-ga