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Table 4 Summary of the demonstrations of the C1C2.

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).

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.

  1. n denotes the number of observations in a dataset, p the number of variables, Δ represents a given subset of the p variables, and λ the ridge regression parameter.