predict_bartMachineArr {bartMachine} | R Documentation |
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 |
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.
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) ##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]) ## End(Not run)