Premium
Test set verification is an essential step in model building
Author(s) -
Quinn Thomas P.,
Le Vuong,
Cardilini Adam P. A.
Publication year - 2021
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.13495
Subject(s) - computer science , workflow , deep learning , artificial intelligence , set (abstract data type) , field (mathematics) , test (biology) , machine learning , test set , data science , software engineering , programming language , database , paleontology , mathematics , pure mathematics , biology
Recently, Christin et al. published an article that reviewed the field of deep learning and offered advice on how to train a deep learning model. We write here to emphasize the importance of model verification, which can help ensure that the model will generalize to new data. Specifically, we discuss the importance of using a test set for model verification, and of defining an explicit research hypothesis. We then present a revised workflow that will help ensure that the accuracy reported for your deep learning model is reliable.