run_svm_mc: SVM multicore tuning.

In autoMrP: Improving MrP with Ensemble Learning

SVM multicore tuning.

Description

run_svm_mc is called from within run_svm. It tunes using multiple cores.

Usage

run_svm_mc(
  y,
  L1.x,
  L2.x,
  L2.eval.unit,
  L2.unit,
  L2.reg,
  form,
  loss.unit,
  loss.fun,
  data,
  cores,
  svm.grid,
  verbose
)

Arguments

y	Outcome variable. A character vector containing the column names of the outcome variable. A character scalar containing the column name of the outcome variable in survey.
L1.x	Individual-level covariates. A character vector containing the column names of the individual-level variables in survey and census used to predict outcome y. Note that geographic unit is specified in argument L2.unit.
L2.x	Context-level covariates. A character vector containing the column names of the context-level variables in survey and census used to predict outcome y.
L2.eval.unit	Geographic unit for the loss function. A character scalar containing the column name of the geographic unit in survey and census.
L2.unit	Geographic unit. A character scalar containing the column name of the geographic unit in survey and census at which outcomes should be aggregated.
L2.reg	Geographic region. A character scalar containing the column name of the geographic region in survey and census by which geographic units are grouped (L2.unit must be nested within L2.reg). Default is NULL.
form	The model formula. A formula object.
loss.unit	Loss function unit. A character-valued scalar indicating whether performance loss should be evaluated at the level of individual respondents (individuals), geographic units (L2 units) or at both levels. Default is c("individuals", "L2 units"). With multiple loss units, parameters are ranked for each loss unit and the loss unit with the lowest rank sum is chosen. Ties are broken according to the order in the search grid.
loss.fun	Loss function. A character-valued scalar indicating whether prediction loss should be measured by the mean squared error (MSE) or the mean absolute error (MAE). Default is MSE.
data	Data for cross-validation. A list of k data.frames, one for each fold to be used in k-fold cross-validation.
cores	The number of cores to be used. An integer indicating the number of processor cores used for parallel computing. Default is 1.
svm.grid	The hyper-parameter search grid. A matrix of all hyper-parameter combinations.
verbose	Verbose output. A logical argument indicating whether or not verbose output should be printed. Default is FALSE.

Value

The cross-validation errors for all models. A list.

autoMrP documentation built on Aug. 17, 2023, 5:07 p.m.