Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Performance-Explainability Framework to Benchmark Machine Learning
  Methods: Application to Multivariate Time Series Classifiers   Our research aims to propose a new performance-explainability analytical
framework to assess and benchmark machine learning methods. The framework
details a set of characteristics that systematize the
performance-explainability assessment of existing machine learning methods. In
order to illustrate the use of the framework, we apply it to benchmark the
current state-of-the-art multivariate time series classifiers.

23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
2
The categories of this paper are: math