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General cross validation scheme applied to evaluate the classification accuracy in all combinations of algorithms and data features.

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posted on 20.04.2017, 17:38 by Raymond Salvador, Joaquim Radua, Erick J. Canales-Rodríguez, Aleix Solanes, Salvador Sarró, José M. Goikolea, Alicia Valiente, Gemma C. Monté, María del Carmen Natividad, Amalia Guerrero-Pedraza, Noemí Moro, Paloma Fernández-Corcuera, Benedikt L. Amann, Teresa Maristany, Eduard Vieta, Peter J. McKenna, Edith Pomarol-Clotet

For most classifiers, cross-validation is used at two levels: at an outer level for training and testing and within each training sample to select the optimal values for the regularization parameters (delta). The effect of nuisance covariates on test data should be regressed out by using coefficients fitted in the training data. Individual performances are given as frequencies of test individuals successfully classified.