bart_predict_for_test_data: Predict for Test Data with Known Outcomes

In bartMachine: Bayesian Additive Regression Trees

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)

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.

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.

Author(s)

Adam Kapelner and Justin Bleich

See Also

predict

Examples

## Not run: 
#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)

## End(Not run)

bartMachine documentation built on July 9, 2023, 5:59 p.m.