This CBDA function generates *max_covs - min_covs* nested models based on the ranking returned by the *Consolidation* function. It also consolidates all the *max_covs - min_covs* workspaces into a single one.
CBDA_Validation(label = "CBDA_package_test", alpha = 0.2, Kcol_min = 5,
Kcol_max = 15, Nrow_min = 30, Nrow_max = 50, misValperc = 0,
M = 3000, N_cores = 1, top = 1000, workspace_directory = tempdir(),
max_covs = 100, min_covs = 5)This is the label appended to RData workspaces generated within the CBDA calls
Percentage of the Big Data to hold off for Validation
Lower bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR)
Upper bound for the percentage of features-columns sampling (used for the Feature Sampling Range - FSR)
Lower bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR)
Upper bound for the percentage of cases-rows sampling (used for the Case Sampling Range - CSR)
Percentage of missing values to introduce in BigData (used just for testing, to mimic real cases).
Number of the BigData subsets on which perform Knockoff Filtering and SuperLearner feature mining
Number of Cores to use in the parallel implementation
Top predictions to select out of the M
Directory where the results and workspaces are saved
Top features to display and include in the Validation Step where nested models are tested
Minimum number of top features to include in the initial model for the Validation Step
value