From: The C1C2: A framework for simultaneous model selection and assessment
Both the C1C2 and repeated K-fold cross-validation performed well at finding the true Δ (even when independent variables are highly correlated and when n <p). |
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The C1C2 and repeated K-fold cross-validation produced reasonable estimates of λ. |
Prior information about the number of important independent variables improves model choice but can reduce the accuracy of generalization error estimates. |
Correlated independent variables and using the genetic algorithm worsened the model choice significantly, but not the generalization error estimates. |
The C1C2 compares favourably with repeated K-fold cross-validation for assessing the generalization error. |