bartMachine (version 1.2.6)

bartMachineCV: Build BART-CV

Description

Builds a BART-CV model by cross-validating over a grid of hyperparameter choices.

Usage

bartMachineCV(X = NULL, y = NULL, Xy = NULL, 
num_tree_cvs = c(50, 200), k_cvs = c(2, 3, 5), 
nu_q_cvs = NULL, k_folds = 5, verbose = FALSE, ...)
build_bart_machine_cv(X = NULL, y = NULL, Xy = NULL, 
num_tree_cvs = c(50, 200), k_cvs = c(2, 3, 5), 
nu_q_cvs = NULL, k_folds = 5, verbose = FALSE, ...)

Value

Returns an object of class ``bartMachine'' with the set of hyperparameters chosen via cross-validation. We also return a matrix ``cv_stats'' which contains the out-of-sample RMSE for each hyperparameter set tried and ``folds'' which gives the fold in which each observation fell across the k-folds.

Arguments

X

Data frame of predictors. Factors are automatically converted to dummies interally.

y

Vector of response variable. If y is numeric or integer, a BART model for regression is built. If y is a factor with two levels, a BART model for classification is built.

Xy

A data frame of predictors and the response. The response column must be named ``y''.

num_tree_cvs

Vector of sizes for the sum-of-trees models to cross-validate over.

k_cvs

Vector of choices for the hyperparameter k to cross-validate over.

nu_q_cvs

Only for regression. List of vectors containing (nu, q) ordered pair choices to cross-validate over. If NULL, then it defaults to the three values list(c(3, 0.9), c(3, 0.99), c(10, 0.75)).

k_folds

Number of folds for cross-validation

verbose

Prints information about progress of the algorithm to the screen.

...

Additional arguments to be passed to bartMachine.

Author

Adam Kapelner and Justin Bleich

References

Adam Kapelner, Justin Bleich (2016). bartMachine: Machine Learning with Bayesian Additive Regression Trees. Journal of Statistical Software, 70(4), 1-40. doi:10.18637/jss.v070.i04

See Also

bartMachine

Examples

if (FALSE) {
#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_cv = bartMachineCV(X, y)

#information about cross-validated model
summary(bart_machine_cv)
}