MTComputeError: Compute (mean) squared error for linear multi-task model.

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

Description

Compute (mean) squared error for linear multi-task model.

Usage

MTComputeError(LMTL.model, Y, X = NULL, task.specific.features = list(),
  pred = NULL, normalize = TRUE, aggregate.tasks = TRUE)

Arguments

LMTL.model	Linear multi-task learning model (list containing B and intercept).
Y	N by K output matrix for every task.
X	N by J1 matrix of features common to all tasks.
task.specific.features	Named 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.
pred	Predicted output matrix. If NULL, compute predictions using input features.
normalize	Compute mean (TRUE) or sum (FALSE).
aggregate.tasks	Aggregate results over all tasks (TRUE) or return task specific errors (FALSE).

Value

The (mean) squared error between predictions for each task and Y.

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