Cross-validation (statistics)

Known as: Hold-out cross-validation, LOOCV, Rotation estimation 
Cross-validation, sometimes called rotation estimation, is a model validation technique for assessing how the results of a statistical analysis will… (More)
Wikipedia

Related topics

Related topics

49 relations
Akaike information criterionArtificial neural networkAutoregressive modelBacktesting
(More)

Broader (2)

Machine learningModel selection

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2010
Highly Cited
2010
Linear Model Selection by Cross-Validation
JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of… (More)
  • table 1
  • table 2
Is this relevant?
Highly Cited
2007
Highly Cited
2007
Improvements on Cross-Validation : The . 632 + Bootstrap Method
Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org… (More)
  • figure 1
  • table 1
  • figure 2
  • figure 3
  • table 2
Is this relevant?
Highly Cited
2007
Highly Cited
2007
Assessment of PLSDA cross validation
Classifying groups of individuals based on their metabolic profile is one of the main topics in metabolomics research. Due to the… (More)
  • figure 1
  • figure 2
  • table 1
  • figure 3
  • figure 4
Is this relevant?
Highly Cited
2005
Highly Cited
2005
Bias in error estimation when using cross-validation for model selection
Cross-validation (CV) is an effective method for estimating the prediction error of a classifier. Some recent articles have… (More)
  • table 1
  • figure 4
  • figure 3
Is this relevant?
Highly Cited
2004
Highly Cited
2004
Is cross-validation valid for small-sample microarray classification?
MOTIVATION Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are… (More)
  • figure 1
Is this relevant?
Highly Cited
2003
Highly Cited
2003
Assessing Model Fit by Cross-Validation
When QSAR models are fitted, it is important to validate any fitted model-to check that it is plausible that its predictions will… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 4
  • figure 5
Is this relevant?
Highly Cited
2001
Highly Cited
2001
Monte Carlo cross validation
In order to choose correctly the dimension of calibration model in chemistry, a new simple and effective method named Ž . Monte… (More)
  • table 1
  • figure 3
  • table 2
  • table 3
  • table 4
Is this relevant?
Highly Cited
1996
Highly Cited
1996
Wavelet shrinkage using cross-validation
Wavelets are orthonormal basis functions with special prop erties that show potential in many areas of mathematics and statistics… (More)
  • figure 1
  • figure 2
  • figure 3
  • table 1
  • table 2
Is this relevant?
Highly Cited
1994
Highly Cited
1994
Neural Network Ensembles, Cross Validation, and Active Learning
Learning of continuous valued functions using neural network ensembles (committees) can give improved accuracy, reliable… (More)
Is this relevant?
Highly Cited
1992
Highly Cited
1992
Cross-Validation of Multivariate
In recent years, the focus of study in smoothing parameter selection for kernel density estimation has been on the univariate… (More)
  • figure 1
  • figure 2
  • figure 3
  • figure 8
  • figure 9
Is this relevant?