Selected article for: "cross validation and prediction accuracy"

Author: Kirillova, Svetlana; Kumar, Suresh; Carugo, Oliviero
Title: Protein Domain Boundary Predictions: A Structural Biology Perspective
  • Document date: 2009_1_21
  • ID: qrnhp1ek_14
    Snippet: The prediction accuracy was validated with a Jack-knife procedure. In statistical prediction, the following three crossvalidation methods are often used to examine a predictor for its effectiveness in practical applications: independent test dataset, sub-sampling test, and Jack-knife test [56] . However, as elucidated in references [26] and [27] , amongst the three cross-validation methods, the Jack-knife test is deemed the most objective that ca.....
    Document: The prediction accuracy was validated with a Jack-knife procedure. In statistical prediction, the following three crossvalidation methods are often used to examine a predictor for its effectiveness in practical applications: independent test dataset, sub-sampling test, and Jack-knife test [56] . However, as elucidated in references [26] and [27] , amongst the three cross-validation methods, the Jack-knife test is deemed the most objective that can always yield a unique result for a given benchmark dataset, and hence has been increasingly used and widely recognized by investigators to examine the accuracy of various predictors [57] [58] [59] [60] [61] [62] [63] [64] [65] [66] .

    Search related documents:
    Co phrase search for related documents
    • independent test dataset and practical application effectiveness: 1
    • independent test dataset and practical application effectiveness predictor: 1
    • independent test dataset and prediction accuracy: 1
    • independent test dataset and statistical prediction: 1
    • independent test dataset and test dataset: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19
    • practical application and prediction accuracy: 1, 2
    • practical application and statistical prediction: 1
    • practical application and test dataset: 1
    • practical application effectiveness and statistical prediction: 1
    • practical application effectiveness and test dataset: 1
    • practical application effectiveness predictor and statistical prediction: 1
    • practical application effectiveness predictor and test dataset: 1
    • prediction accuracy and statistical prediction: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • prediction accuracy and test dataset: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
    • predictor accuracy and test dataset: 1
    • statistical prediction and test dataset: 1, 2, 3
    • test dataset and widely recognize: 1