计算机科学
训练集
线性回归
机器学习
数据挖掘
统计假设检验
人工智能
统计
算法
模式识别(心理学)
数学
摘要
Abstract 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 , where is the number of parameters in a linear regression model that explains the data well.
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