Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: LionForests: Local Interpretation of Random Forests   Towards a future where machine learning systems will integrate into every
aspect of people's lives, researching methods to interpret such systems is
necessary, instead of focusing exclusively on enhancing their performance.
Enriching the trust between these systems and people will accelerate this
integration process. Many medical and retail banking/finance applications use
state-of-the-art machine learning techniques to predict certain aspects of new
instances. Tree ensembles, like random forests, are widely acceptable solutions
on these tasks, while at the same time they are avoided due to their black-box
uninterpretable nature, creating an unreasonable paradox. In this paper, we
provide a methodology for shedding light on the predictions of the misjudged
family of tree ensemble algorithms. Using classic unsupervised learning
techniques and an enhanced similarity metric, to wander among transparent trees
inside a forest following breadcrumbs, the interpretable essence of tree
ensembles arises. An interpretation provided by these systems using our
approach, which we call "LionForests", can be a simple, comprehensive rule.

11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math