EZtune (version 3.1.1)

eztune_cv: Cross Validated Accuracy for Supervised Learning Model

Description

eztune_cv returns the cross-validated loss measures for a model returned by eztune. The function eztune can tune a model using validation data, cross validation, data splitting, or resubstitution. If resubstitution or a data splitting method (via the fast option) is used to tune the model, the accuracy obtained from the function may not be accurate. The function eztune_cv will return cross-validated accuracy measures for any model returned by eztune.

Usage

eztune_cv(x, y, model, cross = 10)

Arguments

x

Matrix or data frame containing the dependent variables used to create the model.

y

Vector of the response used to create the model. Can be either numeric or a factor.

model

An Object of class eztune generated by the function eztune.

cross

Number of folds to use for n-fold cross-validation.

Value

Function returns a numeric value that represents the cross-validated accuracy of the model. Both classification accuracy and the AUC are returned for models with a binary response. MSE and mean absolute error (MAE) are returned for models with a continuous response.

accuracy

Cross-validated classification accuracy.

auc

Cross-validated AUC.

mse

Cross-validated MSE.

mae

Cross-validated MAE.

Examples

# NOT RUN {
library(mlbench)
data(Sonar)
sonar <- Sonar[sample(1:nrow(Sonar), 100), ]

y <- sonar[, 61]
x <- sonar[, 1:10]

sonar_default <- eztune(x, y)
eztune_cv(x, y, sonar_default)

sonar_svm <- eztune(x, y, fast = FALSE, cross = 3)
eztune_cv(x, y, sonar_svm)

sonar_gbm <- eztune(x, y, method = "gbm", fast = 50)
eztune_cv(x, y, sonar_gbm)


# }