Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An imputation method for estimating the learning curve in classification
  problems   The learning curve expresses the error rate of a predictive modeling
procedure as a function of the sample size of the training dataset. It
typically is a decreasing, convex function with a positive limiting value. An
estimate of the learning curve can be used to assess whether a modeling
procedure should be expected to become substantially more accurate if
additional training data become available. This article proposes a new
procedure for estimating learning curves using imputation. We focus on
classification, although the idea is applicable to other predictive modeling
settings. Simulation studies indicate that the learning curve can be estimated
with useful accuracy for a roughly four-fold increase in the size of the
training set relative to the available data, and that the proposed imputation
approach outperforms an alternative estimation approach based on parameterizing
the learning curve. We illustrate the method with an application that predicts
the risk of disease progression for people with chronic lymphocytic leukemia.

20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
14
The categories of this paper are: math-ph
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
27
The categories of this paper are: comp-gas