Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Supervised Machine Learning Techniques: An Overview with Applications to
  Banking   This article provides an overview of Supervised Machine Learning (SML) with a
focus on applications to banking. The SML techniques covered include Bagging
(Random Forest or RF), Boosting (Gradient Boosting Machine or GBM) and Neural
Networks (NNs). We begin with an introduction to ML tasks and techniques. This
is followed by a description of: i) tree-based ensemble algorithms including
Bagging with RF and Boosting with GBMs, ii) Feedforward NNs, iii) a discussion
of hyper-parameter optimization techniques, and iv) machine learning
interpretability. The paper concludes with a comparison of the features of
different ML algorithms. Examples taken from credit risk modeling in banking
are used throughout the paper to illustrate the techniques and interpret the
results of the algorithms.

36
The categories of this paper are: cmp-lg
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics