bart_predict_for_test_data {bartMachine} | R Documentation |
Utility wrapper function for computing out-of-sample metrics for a BART model when the test set outcomes are known.
bart_predict_for_test_data(bart_machine, Xtest, ytest, prob_rule_class = NULL)
bart_machine |
An object of class “bartMachine”. |
Xtest |
Data frame for test data containing rows at which predictions are to be made. Colnames should match that of the training data. |
ytest |
Actual outcomes for test data. |
prob_rule_class |
Threshold for classification. |
For regression models, a list with the following components is returned:
y_hat |
Predictions (as posterior means) for the test observations. |
L1_err |
L1 error for predictions. |
L2_err |
L2 error for predictions. |
rmse |
RMSE for predictions. |
For classification models, a list with the following components is returned:
y_hat |
Class predictions for the test observations. |
p_hat |
Probability estimates for the test observations. |
confusion_matrix |
A confusion matrix for the test observations. |
Adam Kapelner and Justin Bleich
#generate Friedman data set.seed(11) n = 250 p = 5 X = data.frame(matrix(runif(n * p), ncol = p)) y = 10 * sin(pi* X[ ,1] * X[,2]) +20 * (X[,3] -.5)^2 + 10 * X[ ,4] + 5 * X[,5] + rnorm(n) ##split into train and test train_X = X[1 : 200, ] test_X = X[201 : 250, ] train_y = y[1 : 200] test_y = y[201 : 250] ##build BART regression model bart_machine = bartMachine(train_X, train_y) #explore performance on test data oos_perf = bart_predict_for_test_data(bart_machine, test_X, test_y) print(oos_perf$rmse)