cvGBM: Make a crossvalidation using GBM.

In bootfs: Derive Robust Feature Sets for Two or Multiclass Classification Problems

Description

Make a crossvalidation using GBM.

Usage

cvGBM(logX, groupings, DIR, params = list(seed = 123, ncv = 5,
		repeats = 10, ntree = 1000, shrinkage = 0.01,
		interaction.depth = 3, bag.fraction = 0.75,
		train.fraction = 0.75, n.minobsinnode = 3,
		n.cores = 1, verbose = TRUE, jitter = FALSE))

Arguments

logX	The data matrix.
groupings	The list containing the group factors.
DIR	An output directory.
params	A parameter list.

Details

Internal function.

Value

CV result. A list with three elements res, featlist and performance, holding the crossvalidation data, an extracted features list in each cv-iteration and an overall performance object, holding information on the performance (AUC values and a roc curve object that can be plotted). See resultCV for making a summary plot for the CV.

Author(s)

Christian Bender

References

J.H. Friedman (2001). "Greedy Function Approximation: A Gradient Boosting Machine," _Annals of Statistics_ 29(5):1189-1232.

J.H. Friedman (2002). "Stochastic Gradient Boosting," _Computational Statistics and Data Analysis_ 38(4):367-378.

See Also

doCV

Examples

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bootfs documentation built on May 2, 2019, 5:50 p.m.