RunGroupCrossvalidation: Cross-validation wrapper for 'TreeGuidedGroupLasso'.

In tohein/linearMTL: Linear Models for Multi-Task Learning

Description

Perform k-fold cross-validation to find optimal parameters settings.

Usage

RunGroupCrossvalidation(X = NULL, task.specific.features = list(), Y,
  groups, weights.matrix, lambda.vec, num.folds = 10, num.threads = 1,
  verbose = -1, row.weights = NULL, standardize = TRUE,
  fit.intercept = TRUE, ...)

Arguments

X	Column centered N by J input matrix of features common to all tasks.
task.specific.features	List of features which are specific to each task. Each entry contains an N by J2 column-centered matrix for one particular task (where columns are features). List has to be ordered according to the columns of Y.
Y	Column centered N by K output matrix for every task.
groups	V by K matrix determining group membership: Task k in group v iff groups[v,k] == 1 weights.matrix: Numerical matrix where each row is a numerical vector mapping groups to their weight.
weights.matrix	Numerical matrix where each row is a numerical vector mapping groups to their weight.
lambda.vec	Vector of regularization parameters.
num.folds	Number of folds.
num.threads	Number of threads to use.
verbose	(Optional) Integer in -2, -1, 0,1,2. verbose = -2: No output. verbose = -1: Display total elapsed time. verbose = 0: Display elapsed time for every parameter. verbose = 1: Print summary at the end of the optimization. verbose = 2: Print progress during optimization.
row.weights	(Optional) Use weighted MSE.
standardize	(Optional) Default is TRUE. Standardize data (using R function scale()). Coefficients will be returned on original scale.
fit.intercept	(Optional) Default is TRUE. Include intercept.
...	Additional parameters passed to TreeGuidedGroupLasso.

Value

List containing

cv.results	data.frame with cross-validation errors for different parameters.
full.model	Full model trained on the whole data set.

See Also

TreeGuidedGroupLasso

tohein/linearMTL documentation built on May 17, 2019, 8:22 a.m.