RunTBTCrossvalidation: Cross-validation wrapper for task-by-task lasso model.

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

Description

Perform k-fold cross-validation for each task using cv.glmnet.

Usage

RunTBTCrossvalidation(X = NULL, task.specific.features = list(), Y,
  lambda.vec, num.folds = 10, num.threads = 1, verbose = TRUE, ...)

Arguments

X	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 matrix for one particular task (where columns are features). List has to be ordered according to the columns of Y.
Y	N by K output matrix for every task.
lambda.vec	Vector of regularization parameters.
num.folds	Number of folds.
num.threads	Number of threads to use.
verbose	Be verbose.
...	Additional parameters passed to cv.glmnet.

Value

List containing

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

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