bartMachine (version 1.3.4.1)

k_fold_cv: Estimate Out-of-sample Error with K-fold Cross validation

Description

Builds a BART model using a specified set of arguments to build_bart_machine and estimates the out-of-sample performance by using k-fold cross validation.

Usage

k_fold_cv(X, y, k_folds = 5, folds_vec = NULL, verbose = FALSE, ...)

Value

For regression models, a list with the following components is returned:

y_hat

Predictions for the observations computed on the fold for which the observation was omitted from the training set.

L1_err

Aggregate L1 error across the folds.

L2_err

Aggregate L1 error across the folds.

rmse

Aggregate RMSE across the folds.

folds

Vector of indices specifying which fold each observation belonged to.

For classification models, a list with the following components is returned:

y_hat

Class predictions for the observations computed on the fold for which the observation was omitted from the training set.

p_hat

Probability estimates for the observations computed on the fold for which the observation was omitted from the training set.

confusion_matrix

Aggregate confusion matrix across the folds.

misclassification_error

Total misclassification error across the folds.

folds

Vector of indices specifying which fold each observation belonged to.

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.

k_folds

Number of folds to cross-validate over. This argument is ignored if folds_vec is non-null.

folds_vec

An integer vector of indices specifying which fold each observation belongs to.

verbose

Prints information about progress of the algorithm to the screen.

...

Additional arguments to be passed to build_bart_machine.

Author

Adam Kapelner and Justin Bleich

Details

For each fold, a new BART model is trained (using the same set of arguments) and its performance is evaluated on the holdout piece of that fold.

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)

#evaluate default BART on 5 folds
k_fold_val = k_fold_cv(X, y)
print(k_fold_val$rmse)
}