summary.bartMachine {bartMachine} | R Documentation |
bartMachine
object.
Provides a quick summary of the BART model.
## S3 method for class 'bartMachine' summary(object, ...)
object |
An object of class “bartMachine”. |
... |
Parameters that are ignored. |
Gives the version number of the bartMachine
package used to build this bartMachine
object and if the object
models either “regression” or “classification.” Gives the amount of training data and the dimension of feature space. Prints
the amount of time it took to build the model, how many processor cores were used to during its construction, as well as the
number of burn-in and posterior Gibbs samples were used.
If the model is for regression, it prints the estimate of σ^2 before the model was constructed as well as after so the user can inspect how much variance was explained.
If the model was built using the run_in_sample = TRUE
parameter in build_bart_machine
and is for regression, the summary L1,
L2, rmse, Pseudo-R^2 are printed as well as the p-value for the tests of normality and zero-mean noise. If the model is for classification, a confusion matrix is printed.
None.
Adam Kapelner and Justin Bleich
#Regression example #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) ##print out details summary(bart_machine) ##Also, the default print works too bart_machine