Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretability and Explainability: A Machine Learning Zoo Mini-tour   In this review, we examine the problem of designing interpretable and
explainable machine learning models. Interpretability and explainability lie at
the core of many machine learning and statistical applications in medicine,
economics, law, and natural sciences. Although interpretability and
explainability have escaped a clear universal definition, many techniques
motivated by these properties have been developed over the recent 30 years with
the focus currently shifting towards deep learning methods. In this review, we
emphasise the divide between interpretability and explainability and illustrate
these two different research directions with concrete examples of the
state-of-the-art. The review is intended for a general machine learning
audience with interest in exploring the problems of interpretation and
explanation beyond logistic regression or random forest variable importance.
This work is not an exhaustive literature survey, but rather a primer focusing
selectively on certain lines of research which the authors found interesting or
informative.

37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
8
The categories of this paper are: hep-ex
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an