Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Aryana Arsham"'
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-6 (2022)
Abstract Random forests are a popular type of machine learning model, which are relatively robust to overfitting, unlike some other machine learning models, and adequately capture non-linear relationships between an outcome of interest and multiple i
Externí odkaz:
https://doaj.org/article/3d0fc7cb082043f39c7b65a80e1f5717
Publikováno v:
The New England Journal of Statistics in Data Science. :46-61
Random forests are a powerful machine learning tool that capture complex relationships between independent variables and an outcome of interest. Trees built in a random forest are dependent on several hyperparameters, one of the more critical being t
Autor:
Harry M. Cullings, Aryana Arsham, Cato M Milder, Mark P. Little, Helmut Schöllnberger, Richard Wakeford, Gerald M. Kendall
Publikováno v:
Int. J. Radiat. Biol. 97, 866-873 (2021)
Int J Radiat Biol
Int J Radiat Biol
Cancer risk is thought to be the main health risk of low-level ionizing radiation exposure (Committee to Assess Health Risks from Exposure to Low Levels of Ionizing Radiation 2006; United Nations S...
Publikováno v:
Journal of Statistical Theory and Practice. 16