bartMachine (version 1.2.3)

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

"predict"(object, new_data, type, prob_rule_class, ...)

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.

...

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

Examples

#Regression example
## Not run: 
# #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)
# ## End(Not run)