Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SMACE: A New Method for the Interpretability of Composite Decision
  Systems   Interpretability is a pressing issue for decision systems. Many post hoc
methods have been proposed to explain the predictions of a single machine
learning model. However, business processes and decision systems are rarely
centered around a unique model. These systems combine multiple models that
produce key predictions, and then apply decision rules to generate the final
decision. To explain such decisions, we propose the Semi-Model-Agnostic
Contextual Explainer (SMACE), a new interpretability method that combines a
geometric approach for decision rules with existing interpretability methods
for machine learning models to generate an intuitive feature ranking tailored
to the end user. We show that established model-agnostic approaches produce
poor results on tabular data in this setting, in particular giving the same
importance to several features, whereas SMACE can rank them in a meaningful
way.

22
The categories of this paper are: chao-dyn
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
27
The categories of this paper are: comp-gas
26
The categories of this paper are: bayes-an
8
The categories of this paper are: hep-ex
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
12
The categories of this paper are: nucl-th
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg