Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Automating biomedical data science through tree-based pipeline
  optimization   Over the past decade, data science and machine learning has grown from a
mysterious art form to a staple tool across a variety of fields in academia,
business, and government. In this paper, we introduce the concept of tree-based
pipeline optimization for automating one of the most tedious parts of machine
learning---pipeline design. We implement a Tree-based Pipeline Optimization
Tool (TPOT) and demonstrate its effectiveness on a series of simulated and
real-world genetic data sets. In particular, we show that TPOT can build
machine learning pipelines that achieve competitive classification accuracy and
discover novel pipeline operators---such as synthetic feature
constructors---that significantly improve classification accuracy on these data
sets. We also highlight the current challenges to pipeline optimization, such
as the tendency to produce pipelines that overfit the data, and suggest future
research paths to overcome these challenges. As such, this work represents an
early step toward fully automating machine learning pipeline design.

29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
2
The categories of this paper are: math
19
The categories of this paper are: eess
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
33
The categories of this paper are: plasm-ph
27
The categories of this paper are: comp-gas