Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SLISEMAP: Explainable Dimensionality Reduction   Existing explanation methods for black-box supervised learning models
generally work by building local models that explain the models behaviour for a
particular data item. It is possible to make global explanations, but the
explanations may have low fidelity for complex models. Most of the prior work
on explainable models has been focused on classification problems, with less
attention on regression.
  We propose a new manifold visualization method, SLISEMAP, that at the same
time finds local explanations for all of the data items and builds a
two-dimensional visualization of model space such that the data items explained
by the same model are projected nearby. We provide an open source
implementation of our methods, implemented by using GPU-optimized PyTorch
library. SLISEMAP works both on classification and regression models.
  We compare SLISEMAP to most popular dimensionality reduction methods and some
local explanation methods. We provide mathematical derivation of our problem
and show that SLISEMAP provides fast and stable visualizations that can be used
to explain and understand black box regression and classification models.

15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex
9
The categories of this paper are: nlin
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th