Create an array of BART models for the same data.
If BART creates models that are variable, running many on the same dataset and averaging is a good strategy. This function is a convenience method for this procedure.
bartMachineArr(bart_machine, R = 10)
bart_machine |
An object of class “bartMachine”. |
R |
The number of replicated BART models in the array. |
A bartMachineArr object which is just a list of the R bartMachine models.
Adam Kapelner
#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) #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) ## End(Not run)
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