Make a prediction on data using a BART object

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

Usage

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

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.

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.

See also

bart_predict_for_test_data

Author

Adam Kapelner and Justin Bleich

Examples

#Regression example
if (FALSE) { # \dontrun{
#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)
} # }