Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Where are we now? A large benchmark study of recent symbolic regression
  methods   In this paper we provide a broad benchmarking of recent genetic programming
approaches to symbolic regression in the context of state of the art machine
learning approaches. We use a set of nearly 100 regression benchmark problems
culled from open source repositories across the web. We conduct a rigorous
benchmarking of four recent symbolic regression approaches as well as nine
machine learning approaches from scikit-learn. The results suggest that
symbolic regression performs strongly compared to state-of-the-art gradient
boosting algorithms, although in terms of running times is among the slowest of
the available methodologies. We discuss the results in detail and point to
future research directions that may allow symbolic regression to gain wider
adoption in the machine learning community.

5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
22
The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
6
The categories of this paper are: astro-ph
7
The categories of this paper are: hep-th
25
The categories of this paper are: patt-sol
12
The categories of this paper are: nucl-th
29
The categories of this paper are: funct-an
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat