1d:T7bc,# cal_predict A function to make predictions and calculate test error given an object returned by Boost and test data ```r cal_predict(model, x_test, y_test) ``` ## Arguments - `model`: an object returned by Boost - `x_test`: predictor matrix for test data (matrix/dataframe) - `y_test`: response vector for test data (vector/dataframe) ## Returns A list with with the following components: - **f_t_test**: predicted values with model at the early stopping iteration using x_test as the predictors - **err_test**: a matrix of test errors before and at the early stopping iteration (returned if make_prediction = TRUE in control); the matrix dimension is the early stopping iteration by the number of error types (matches the `error` argument in the input); each row corresponds to the test errors at each iteration - **f_test**: a matrix of test function estimates at all iterations (returned if save_f = TRUE in control) - **value**: a vector of test errors evaluated at the early stopping iteration ## Details A function to make predictions and calculate test error given an object returned by Boost and test data ## 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 = Boost(x_train = xtrain, y_train = ytrain, x_val = xval, y_val = yval, type = "RRBoost", error = "rmse", y_init = "LADTree", max_depth = 1, niter = 1000, control = Boost.control(max_depth_init = 2, min_leaf_size_init = 20, save_tree = TRUE, make_prediction = FALSE, cal_imp = FALSE)) prediction <- cal_predict(model, x_test = xtest, y_test = ytest) ``` ## Author(s) Xiaomeng Ju, xmengju@stat.ubc.ca

cal_predict function