# Tuning and control parameters for the robust boosting algorithm Tuning and control parameters for the RRBoost robust boosting algorithm, including the initial fit. ```r 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`) ## Returns A list of all input parameters ## Details Various tuning and control parameters for the RRBoost robust boosting algorithm implemented in the function `Boost`, including options for the initial fit. ## Examples ```r 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 = 3, ## to keep the running time low control = my.control) ``` ## Author(s) Xiaomeng Ju, xiaomeng.ju@stat.ubc.ca

Boost.control function