Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Evaluating Nonlinear Decision Trees for Binary Classification Tasks with
  Other Existing Methods   Classification of datasets into two or more distinct classes is an important
machine learning task. Many methods are able to classify binary classification
tasks with a very high accuracy on test data, but cannot provide any easily
interpretable explanation for users to have a deeper understanding of reasons
for the split of data into two classes. In this paper, we highlight and
evaluate a recently proposed nonlinear decision tree approach with a number of
commonly used classification methods on a number of datasets involving a few to
a large number of features. The study reveals key issues such as effect of
classification on the method's parameter values, complexity of the classifier
versus achieved accuracy, and interpretability of resulting classifiers.

31
The categories of this paper are: ao-sci
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin