bart_predict_for_test_data: Predict for Test Data with Known Outcomes

Description

Utility wrapper function for computing out-of-sample metrics for a BART model when the test set outcomes are known.

Usage

bart_predict_for_test_data(bart_machine, Xtest, ytest, prob_rule_class = NULL)

Value

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.

Arguments

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.

Author

Adam Kapelner and Justin Bleich

Examples

Run this code

if (FALSE) {
#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)
}