random.CVind: Cross validation index generator
In gfboost: Gradient-Free Gradient Boosting

Description Usage Arguments Details Value

Simple auxiliary function for randomly generating the indices for training, validation and test data for cross validation.

1	random.CVind(n, ncmb, nval, CV)

`n`	Number of observations (rows).
`ncmb`	Number of training samples for the SingBoost models in CMB. Must be an integer between 1 and n.
`nval`	Number of validation samples in the CMB aggregation procedure. Must be an integer between 1 and n-n_{cmb}-1.
`CV`	Number of cross validation steps. Must be a positive integer.

The data set consists of $n$ observations. n_{cmb} of them are used for the CMB aggregation procedure. Note that within CMB itself, only a subset of these observations may be used for SingBoost training. The Stability Selection is based on the validation set consisting of n_{val} observations. The cross-validated loss of the final model is evaluated on the test data set with n-n_{cmb}-n_{val} observations. Clearly, all data sets need to be disjoint.

CVind

List of row indices for training, validation and test data for each cross validation loop.

gfboost documentation built on Jan. 7, 2022, 5:06 p.m.