Boost.control {RRBoost}

Tuning and control parameters for the robust boosting algorithm

Description

Tuning and control parameters for the RRBoost robust boosting algorithm, including the initial fit.

Usage

Boost.control(
  n_init = 100,
  eff_m = 0.95,
  bb = 0.5,
  trim_prop = NULL,
  trim_c = 3,
  max_depth_init = 3,
  min_leaf_size_init = 10,
  cal_imp = TRUE,
  save_f = FALSE,
  make_prediction = TRUE,
  save_tree = FALSE,
  precision = 4,
  shrinkage = 1,
  trace = FALSE
)

Arguments

n_init

number of iterations for the SBoost step of RRBoost ($T_1,max$) (int)

eff_m

scalar between 0 and 1 indicating the efficiency (measured in a linear model with Gaussian errors) of Tukey's loss function used in the 2nd stage of RRBoost.

bb

breakdown point of the M-scale estimator used in the SBoost step (numeric)

trim_prop

trimming proportion if 'trmse' is used as the performance metric (numeric). 'trmse' calculates the root-mean-square error of residuals (r) of which |r| < quantile(|r|, 1-trim_prop) (e.g. trim_prop = 0.1 ignores 10% of the data and calculates RMSE of residuals whose absolute values are below 90% quantile of |r|). If both trim_prop and trim_c are specified, trim_c will be used.

trim_c

the trimming constant if 'trmse' is used as the performance metric (numeric, defaults to 3). 'trmse' calculates the root-mean-square error of the residuals (r) between median(r) + trim_c mad(r) and median(r) - trim_c mad(r). If both trim_prop and trim_c are specified, trim_c will be used.

max_depth_init

the maximum depth of the initial LADTtree (numeric, defaults to 3)

min_leaf_size_init

the minimum number of observations per node of the initial LADTtree (numeric, defaults to 10)

cal_imp

logical indicating whether to calculate variable importance (defaults to TRUE)

save_f

logical indicating whether to save the function estimates at all iterations (defaults to FALSE)

make_prediction

logical indicating whether to make predictions using x_test (defaults to TRUE)

save_tree

logical indicating whether to save trees at all iterations (defaults to FALSE)

precision

number of significant digits to keep when using validation error to calculate early stopping time (numeric, defaults to 4)

shrinkage

shrinkage parameter in boosting (numeric, defaults to 1 which corresponds to no shrinkage)

trace

logical indicating whether to print the number of completed iterations and for RRBoost the completed combinations of LADTree hyperparameters for monitoring progress (defaults to FALSE)

Details

Various tuning and control parameters for the RRBoost robust boosting algorithm implemented in the function Boost, including options for the initial fit.

Value

A list of all input parameters

Author(s)

Xiaomeng Ju, [e-mail]

Examples

data(airfoil)
n <- nrow(airfoil)
n0 <- floor( 0.2 * n )
set.seed(123)
idx_test <- sample(n, n0)
idx_train <- sample((1:n)[-idx_test], floor( 0.6 * n ) )
idx_val <- (1:n)[ -c(idx_test, idx_train) ]
xx <- airfoil[, -6]
yy <- airfoil$y
xtrain <- xx[ idx_train, ]
ytrain <- yy[ idx_train ]
xval <- xx[ idx_val, ]
yval <- yy[ idx_val ]
xtest <- xx[ idx_test, ]
ytest <- yy[ idx_test ]
my.control <- Boost.control(max_depth_init = 2,
    min_leaf_size_init = 20, make_prediction =  TRUE,
    cal_imp = FALSE)
model_RRBoost_LADTree = Boost(x_train = xtrain, y_train = ytrain,
    x_val = xval, y_val = yval, x_test = xtest, y_test = ytest,
    type = "RRBoost", error = "rmse", y_init = "LADTree",
    max_depth = 1, niter = 10, ## to keep the running time low
    control = my.control)