run_gb {autoMrP} | R Documentation |
run_gb
is a wrapper function that applies the gradient boosting
classifier to data provided by the user, evaluates prediction performance,
and chooses the best-performing model.
run_gb( y, L1.x, L2.x, L2.eval.unit, L2.unit, L2.reg, loss.unit, loss.fun, interaction.depth, shrinkage, n.trees.init, n.trees.increase, n.trees.max, cores = cores, n.minobsinnode, data, verbose )
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 |
L1.x |
Individual-level covariates. A character vector containing the
column names of the individual-level variables in |
L2.x |
Context-level covariates. A character vector containing the
column names of the context-level variables in |
L2.eval.unit |
Geographic unit. A character scalar containing the column
name of the geographic unit in |
L2.unit |
Geographic unit. A character scalar containing the column
name of the geographic unit in |
L2.reg |
Geographic region. A character scalar containing the column
name of the geographic region in |
loss.unit |
Loss function unit. A character-valued scalar indicating
whether performance loss should be evaluated at the level of individual
respondents ( |
loss.fun |
Loss function. A character-valued scalar indicating whether
prediction loss should be measured by the mean squared error ( |
interaction.depth |
GB interaction depth. An integer-valued vector
whose values specify the interaction depth of GB. The interaction depth
defines the maximum depth of each tree grown (i.e., the maximum level of
variable interactions). Default is |
shrinkage |
GB learning rate. A numeric vector whose values specify the
learning rate or step-size reduction of GB. Values between 0.001
and 0.1 usually work, but a smaller learning rate typically requires
more trees. Default is |
n.trees.init |
GB initial total number of trees. An integer-valued scalar specifying the initial number of total trees to fit by GB. Default is 50. |
n.trees.increase |
GB increase in total number of trees. An
integer-valued scalar specifying by how many trees the total number of
trees to fit should be increased (until |
n.trees.max |
GB maximum number of trees. An integer-valued scalar
specifying the maximum number of trees to fit by GB or an integer-valued
vector of length |
cores |
The number of cores to be used. An integer indicating the number of processor cores used for parallel computing. Default is 1. |
n.minobsinnode |
GB minimum number of observations in the terminal nodes. An integer-valued scalar specifying the minimum number of observations that each terminal node of the trees must contain. Default is 5. |
data |
Data for cross-validation. A |
verbose |
Verbose output. A logical argument indicating whether or not
verbose output should be printed. Default is |
The tuned gradient boosting parameters. A list with three elements:
interaction_depth
contains the interaction depth parameter,
shrinkage
contains the learning rate, n_trees
the number of
trees to be grown.