Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast and Informative Model Selection using Learning Curve
  Cross-Validation   Common cross-validation (CV) methods like k-fold cross-validation or
Monte-Carlo cross-validation estimate the predictive performance of a learner
by repeatedly training it on a large portion of the given data and testing on
the remaining data. These techniques have two major drawbacks. First, they can
be unnecessarily slow on large datasets. Second, beyond an estimation of the
final performance, they give almost no insights into the learning process of
the validated algorithm. In this paper, we present a new approach for
validation based on learning curves (LCCV). Instead of creating train-test
splits with a large portion of training data, LCCV iteratively increases the
number of instances used for training. In the context of model selection, it
discards models that are very unlikely to become competitive. We run a large
scale experiment on the 67 datasets from the AutoML benchmark and empirically
show that in over 90% of the cases using LCCV leads to similar performance (at
most 1.5% difference) as using 5/10-fold CV. However, it yields substantial
runtime reductions of over 20% on average. Additionally, it provides important
insights, which for example allow assessing the benefits of acquiring more
data. These results are orthogonal to other advances in the field of AutoML.

28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
7
The categories of this paper are: hep-th
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
34
The categories of this paper are: chem-ph
27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
6
The categories of this paper are: astro-ph