How to avoid machine learning pitfalls: a guide for academic researchers
M. Lones. (2021)cite arxiv:2108.02497Comment: 17 pages.
Аннотация
This document gives a concise outline of some of the common mistakes that
occur when using machine learning techniques, and what can be done to avoid
them. It is intended primarily as a guide for research students, and focuses on
issues that are of particular concern within academic research, such as the
need to do rigorous comparisons and reach valid conclusions. It covers five
stages of the machine learning process: what to do before model building, how
to reliably build models, how to robustly evaluate models, how to compare
models fairly, and how to report results.
Описание
[2108.02497] How to avoid machine learning pitfalls: a guide for academic researchers
%0 Generic
%1 lones2021avoid
%A Lones, Michael A.
%D 2021
%K deep-learning guide machine-learning
%T How to avoid machine learning pitfalls: a guide for academic researchers
%U http://arxiv.org/abs/2108.02497
%X This document gives a concise outline of some of the common mistakes that
occur when using machine learning techniques, and what can be done to avoid
them. It is intended primarily as a guide for research students, and focuses on
issues that are of particular concern within academic research, such as the
need to do rigorous comparisons and reach valid conclusions. It covers five
stages of the machine learning process: what to do before model building, how
to reliably build models, how to robustly evaluate models, how to compare
models fairly, and how to report results.
@misc{lones2021avoid,
abstract = {This document gives a concise outline of some of the common mistakes that
occur when using machine learning techniques, and what can be done to avoid
them. It is intended primarily as a guide for research students, and focuses on
issues that are of particular concern within academic research, such as the
need to do rigorous comparisons and reach valid conclusions. It covers five
stages of the machine learning process: what to do before model building, how
to reliably build models, how to robustly evaluate models, how to compare
models fairly, and how to report results.},
added-at = {2021-08-23T19:56:07.000+0200},
author = {Lones, Michael A.},
biburl = {https://www.bibsonomy.org/bibtex/2f070ab8532e339700bbfb4a603555691/zgcarvalho},
description = {[2108.02497] How to avoid machine learning pitfalls: a guide for academic researchers},
interhash = {776ce24637b7137cad31739de9087853},
intrahash = {f070ab8532e339700bbfb4a603555691},
keywords = {deep-learning guide machine-learning},
note = {cite arxiv:2108.02497Comment: 17 pages},
timestamp = {2021-08-23T19:56:07.000+0200},
title = {How to avoid machine learning pitfalls: a guide for academic researchers},
url = {http://arxiv.org/abs/2108.02497},
year = 2021
}