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 splitting ratio is $\sqrt{p}:1$, where $p$ is the number of parameters in a linear regression model that explains the data well.
翻译:通常的做法是将数据集分成培训和测试组,然后才安装统计或机器学习模型。 但是,对于培训和测试应使用多少数据,没有明确的指导。 在本篇文章中,我们显示最佳的分割比率是$\ sqrt{p}:1美元,其中P$是精确解释数据的线性回归模型参数数。