# Robust Boosting for regression with initialization parameters chosen on a validation set A function to fit RRBoost (see also `Boost`) where the initialization parameters are chosen based on the performance on the validation set. ```r Boost.validation( x_train, y_train, x_val, y_val, x_test, y_test, type = "RRBoost", error = c("rmse", "aad"), niter = 1000, max_depth = 1, y_init = "LADTree", max_depth_init_set = c(1, 2, 3, 4), min_leaf_size_init_set = c(10, 20, 30), control = Boost.control() ) ``` ## Arguments - `x_train`: predictor matrix for training data (matrix/dataframe) - `y_train`: response vector for training data (vector/dataframe) - `x_val`: predictor matrix for validation data (matrix/dataframe) - `y_val`: response vector for validation data (vector/dataframe) - `x_test`: predictor matrix for test data (matrix/dataframe, optional, required when `make_prediction` in control is `TRUE`) - `y_test`: response vector for test data (vector/dataframe, optional, required when `make_prediction` in control is `TRUE`) - `type`: type of the boosting method: "L2Boost", "LADBoost", "MBoost", "Robloss", "SBoost", "RRBoost" (character string) - `error`: a character string (or vector of character strings) indicating the types of error metrics to be evaluated on the test set. Valid options are: "rmse" (root mean squared error), "aad" (average absulute deviation), and "trmse" (trimmed root mean squared error) - `niter`: number of iterations (for RRBoost T_1,max + T_2,max) (numeric) - `max_depth`: the maximum depth of the tree learners (numeric) - `y_init`: a string indicating the initial estimator to be used. Valid options are: "median" or "LADTree" (character string) - `max_depth_init_set`: a vector of possible values of the maximum depth of the initial LADTree that the algorithm choses from - `min_leaf_size_init_set`: a vector of possible values of the minimum observations per node of the initial LADTree that the algorithm choses from - `control`: a named list of control parameters, as returned by `Boost.control` ## Returns A list with components - **the components of model**: an object returned by Boost that is trained with selected initialization parameters - **param**: a vector of selected initialization parameters (return (0,0) if selected initialization is the median of the training responses) ## Details This function runs the RRBoost algorithm (see `Boost`) on different combinations of the parameters for the initial fit, and chooses the optimal set based on the performance on the validation set. ## 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 ] model_RRBoost_cv_LADTree = Boost.validation(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 = 1000, max_depth_init_set = 1:5, min_leaf_size_init_set = c(10,20,30), control = Boost.control(make_prediction = TRUE, cal_imp = TRUE)) ``` ## See Also `Boost`, `Boost.control`. ## Author(s) Xiaomeng Ju, xmengju@stat.ubc.ca

Boost.validation function