Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MDL-motivated compression of GLM ensembles increases interpretability
  and retains predictive power   Over the years, ensemble methods have become a staple of machine learning.
Similarly, generalized linear models (GLMs) have become very popular for a wide
variety of statistical inference tasks. The former have been shown to enhance
out- of-sample predictive power and the latter possess easy interpretability.
Recently, ensembles of GLMs have been proposed as a possibility. On the
downside, this approach loses the interpretability that GLMs possess. We show
that minimum description length (MDL)-motivated compression of the inferred
ensembles can be used to recover interpretability without much, if any,
downside to performance and illustrate on a number of standard classification
data sets.

23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
1
The categories of this paper are: cs
25
The categories of this paper are: patt-sol
28
The categories of this paper are: alg-geom
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th