Add to ORKGthat leave-one-out cross-validation is not subject to the “no-free-lunch ” criticism. Despite this optimistic conclusion, we show here that cross-validation has very poor performances for the selection of linear models as compared to classic statistical tests. We conclude that the statistical tests are preferable to cross-validation for linear as well as for non linear model selection. 1
<p>In each iteration, data are divided into training and test sets. Before training, another (inner)...
We demonstrate the consistency of cross-validation for comparing multiple density estimators using s...
With the increasing size of today’s data sets, finding the right parameter configuration in model se...
We consider the problem of model (or variable) selection in the classical regression model based on ...
This article gives a robust technique for model selection in regression models, an important aspect ...
For the problem of model selection, full cross-validation has been proposed as alternative criterion...
For the problem of model selection, full cross-validation has been proposed as an alternative criter...
Cross-validation (CV) methods are popular for selecting the tuning parameter in the high-dimensional...
A natural technique to select variables in the context of generalized linear models is to use a step...
Published in Statistics Surveys (2010) 4, 40-79International audienceUsed to estimate the risk of an...
Cross-validation is the process of comparing a model’s predictions to data that were not used in the...
A linear mixed model is a useful technique to explain observations by regarding them as realizations...
When selecting a classification algorithm to be applied to a particular problem, one has to simultan...
Model selection strategies for machine learning algorithms typically involve the numerical optimisat...
Appealing due to its universality, cross-validation is an ubiquitous tool for model tuning and selec...
<p>In each iteration, data are divided into training and test sets. Before training, another (inner)...
We demonstrate the consistency of cross-validation for comparing multiple density estimators using s...
With the increasing size of today’s data sets, finding the right parameter configuration in model se...
We consider the problem of model (or variable) selection in the classical regression model based on ...
This article gives a robust technique for model selection in regression models, an important aspect ...
For the problem of model selection, full cross-validation has been proposed as alternative criterion...
For the problem of model selection, full cross-validation has been proposed as an alternative criter...
Cross-validation (CV) methods are popular for selecting the tuning parameter in the high-dimensional...
A natural technique to select variables in the context of generalized linear models is to use a step...
Published in Statistics Surveys (2010) 4, 40-79International audienceUsed to estimate the risk of an...
Cross-validation is the process of comparing a model’s predictions to data that were not used in the...
A linear mixed model is a useful technique to explain observations by regarding them as realizations...
When selecting a classification algorithm to be applied to a particular problem, one has to simultan...
Model selection strategies for machine learning algorithms typically involve the numerical optimisat...
Appealing due to its universality, cross-validation is an ubiquitous tool for model tuning and selec...
<p>In each iteration, data are divided into training and test sets. Before training, another (inner)...
We demonstrate the consistency of cross-validation for comparing multiple density estimators using s...
With the increasing size of today’s data sets, finding the right parameter configuration in model se...