Makes a prediction on new data given an array of fitted BART model for regression or classification. If BART creates models that are variable, running many and averaging is a good strategy. It is well known that the Gibbs sampler gets locked into local modes at times. This is a way to average over many chains.

`predict_bartMachineArr(object, new_data, ...)`

object

An object of class ``bartMachineArr''.

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.

...

Not supported. Note that parameters `type`

and `prob_rule_class`

for
`predict.bartMachine`

are not supported.

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.

# NOT RUN { #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) bart_machine_arr = bartMachineArr(bart_machine) ##make predictions on the training data y_hat = predict(bart_machine_arr, 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) bart_machine_arr = bartMachineArr(bart_machine) ##make probability predictions on the training data p_hat = predict_bartMachineArr(bart_machine_arr, iris2[ ,1:4]) # } # NOT RUN { # }