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
Splitting a continuous variable into two groups at its median is sometimes used in data analysis. On...
To perform inference after model selection, we propose controlling the selective type I error; i.e.,...
International audienceWe investigate the class of splitting distributions as the composition of a si...
Abstract Background We consider the problem of designing a study to develop a predictive classifier ...
Data splitting divides the training data set into two sets H and the validation set V.Data splitting...
Abstract: We introduce Domain Splitting as a new tool for regression analysis. This device correspon...
The datasets that appear in publications are curated and have been split into training, testing and ...
This paper uses techniques from Random Matrix Theory to find the ideal training-testing data split f...
Background: The bootstrap can be alternative to cross-validation as a training/test set splitting me...
The recently introduced framework of universal inference provides a new approach to constructing hyp...
When learning a dependence from data, to avoid overfitting, it is important to divide the data into ...
We consider simulation studies on supervised learning which measure the performance of a classifica...
<p>Thus, the use of gradient boosted trees requires a careful tuning of the parameters of the algori...
Data splitting divides data into two parts. One part is reserved for model selection. In some applic...
Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various di...
Splitting a continuous variable into two groups at its median is sometimes used in data analysis. On...
To perform inference after model selection, we propose controlling the selective type I error; i.e.,...
International audienceWe investigate the class of splitting distributions as the composition of a si...
Abstract Background We consider the problem of designing a study to develop a predictive classifier ...
Data splitting divides the training data set into two sets H and the validation set V.Data splitting...
Abstract: We introduce Domain Splitting as a new tool for regression analysis. This device correspon...
The datasets that appear in publications are curated and have been split into training, testing and ...
This paper uses techniques from Random Matrix Theory to find the ideal training-testing data split f...
Background: The bootstrap can be alternative to cross-validation as a training/test set splitting me...
The recently introduced framework of universal inference provides a new approach to constructing hyp...
When learning a dependence from data, to avoid overfitting, it is important to divide the data into ...
We consider simulation studies on supervised learning which measure the performance of a classifica...
<p>Thus, the use of gradient boosted trees requires a careful tuning of the parameters of the algori...
Data splitting divides data into two parts. One part is reserved for model selection. In some applic...
Regressions and meta-regressions are widely used to estimate patterns and effect sizes in various di...
Splitting a continuous variable into two groups at its median is sometimes used in data analysis. On...
To perform inference after model selection, we propose controlling the selective type I error; i.e.,...
International audienceWe investigate the class of splitting distributions as the composition of a si...
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