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

```
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, ...)

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`

.

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.

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

# 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 { # }