Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Extending Models Via Gradient Boosting: An Application to Mendelian
  Models   Improving existing widely-adopted prediction models is often a more efficient
and robust way towards progress than training new models from scratch. Existing
models may (a) incorporate complex mechanistic knowledge, (b) leverage
proprietary information and, (c) have surmounted barriers to adoption. Compared
to model training, model improvement and modification receive little attention.
In this paper we propose a general approach to model improvement: we combine
gradient boosting with any previously developed model to improve model
performance while retaining important existing characteristics. To exemplify,
we consider the context of Mendelian models, which estimate the probability of
carrying genetic mutations that confer susceptibility to disease by using
family pedigrees and health histories of family members. Via simulations we
show that integration of gradient boosting with an existing Mendelian model can
produce an improved model that outperforms both that model and the model built
using gradient boosting alone. We illustrate the approach on genetic testing
data from the USC-Stanford Cancer Genetics Hereditary Cancer Panel (HCP) study.

14
The categories of this paper are: math-ph
13
The categories of this paper are: quant-ph
19
The categories of this paper are: eess
1
The categories of this paper are: cs
32
The categories of this paper are: atom-ph
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg