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, ...)

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.

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.

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

# 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_cv = bartMachineCV(X, y)

#information about cross-validated model
summary(bart_machine_cv)
# }
# NOT RUN {
# }