Optimal ratio for data splitting

被引：300
|
作者：
Joseph, V. Roshan [1 ]
机构：
[1] Georgia Inst Technol, H Milton Stewart Sch Ind & Syst Engn, Atlanta, GA 30332 USA
来源：
STATISTICAL ANALYSIS AND DATA MINING | 2022年 / 15卷 / 04期
基金：
美国国家科学基金会;
关键词：
testing; training; validation; CALIBRATION; VALIDATION; MODELS;
D O I：
10.1002/sam.11583
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
It is common to split a dataset into training and testing sets before fitting a statistical or machine learning model. However, there is no clear guidance on how much data should be used for training and testing. In this article, we show that the optimal training/testing splitting ratio is root p : 1, where p is the number of parameters in a linear regression model that explains the data well.
引用
收藏
页码：531 / 538
页数：8
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