predict.bartMachine: Make a prediction on data using a BART object

Description

Makes a prediction on new data given a fitted BART model for regression or classification.

Usage

# S3 method for bartMachine
predict(object, new_data, type = "prob", prob_rule_class = NULL, verbose = TRUE, ...)

Value

If regression, a numeric vector of y_hat, the best guess as to the response. If classification and type = ``prob'', a numeric vector of p_hat, the best guess as to the probability of the response class being the ''positive'' class. If classification and type = ''class'', a character vector of the best guess of the response's class labels.

Arguments

object: An object of class ``bartMachine''.
new_data: A data frame where each row is an observation to predict. The column names should be the same as the column names of the training data.
type: Only relevant if the bartMachine model is classification. The type can be ``prob'' which will return the estimate of \(P(Y = 1)\)(the ``positive'' class) or ``class'' which will return the best guess as to the class of the object, in the original label, based on if the probability estimate is greater than prob_rule_class. Default is ``prob.''
prob_rule_class: The rule to determine when the class estimate is \(Y = 1\) (the ``positive'' class) based on the probability estimate. This defaults to what was originally specified in the bart_machine object.
verbose: Prints out prediction-related messages. Currently in use only for probability predictions to let the user know which class is being predicted. Default is TRUE.
...: Parameters that are ignored.

Author

Adam Kapelner and Justin Bleich

Examples

Run this code

#Regression example
if (FALSE) {
#generate Friedman data
set.seed(11)
n  = 200 
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)

##build BART regression model
bart_machine = bartMachine(X, y)

##make predictions on the training data
y_hat = predict(bart_machine, X)

#Classification example
data(iris)
iris2 = iris[51 : 150, ] #do not include the third type of flower for this example
iris2$Species = factor(iris2$Species)  
bart_machine = bartMachine(iris2[ ,1:4], iris2$Species)

##make probability predictions on the training data
p_hat = predict(bart_machine, X)

##make class predictions on test data
y_hat_class = predict(bart_machine, X, type = "class")

##make class predictions on test data conservatively for ''versicolor''
y_hat_class_conservative = predict(bart_machine, X, type = "class", prob_rule_class = 0.9)
}