summary.bartMachine: Summarizes information about a 'bartMachine' object.

In bartMachine: Bayesian Additive Regression Trees

Summarizes information about a bartMachine object.

Description

Provides a quick summary of the BART model.

Usage

## S3 method for class 'bartMachine'
summary(object, ...)

Arguments

object	An object of class “bartMachine”.
...	Parameters that are ignored.

Details

Gives the version number of the bartMachine package used to build this additiveBartMachine 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 \sigma^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.

Value

None.

Author(s)

Adam Kapelner

Examples

## Not run: 
#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

## End(Not run)

bartMachine documentation built on July 9, 2023, 5:59 p.m.