Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gradient boosting machines and careful pre-processing work best: ASHRAE
  Great Energy Predictor III lessons learned   The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late
2019 as one of the largest machine learning competitions ever held focused on
building performance. It was hosted on the Kaggle platform and resulted in
39,402 prediction submissions, with the top five teams splitting $25,000 in
prize money. This paper outlines lessons learned from participants, mainly from
teams who scored in the top 5% of the competition. Various insights were gained
from their experience through an online survey, analysis of publicly shared
submissions and notebooks, and the documentation of the winning teams. The
top-performing solutions mostly used ensembles of Gradient Boosting Machine
(GBM) tree-based models, with the LightGBM package being the most popular. The
survey participants indicated that the preprocessing and feature extraction
phases were the most important aspects of creating the best modeling approach.
All the survey respondents used Python as their primary modeling tool, and it
was common to use Jupyter-style Notebooks as development environments. These
conclusions are essential to help steer the research and practical
implementation of building energy meter prediction in the future.

31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
0
The categories of this paper are: hep-ph
13
The categories of this paper are: quant-ph
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom