randomUniformForest (version 1.1.0)

generic.cv: Generic k-fold cross-validation

Description

Performs k-fold cross-validation 'n' times for any specified algorithm, using two of many metrics(test error, AUC, precision,...)

Usage

generic.cv(X, Y, 
nTimes = 1, 
k = 10, 
seed = 2014, 
regression = TRUE, 
genericAlgo = NULL, 
specificPredictFunction = NULL, 
metrics = c("none", "AUC", "precision", "F-score", "L1", "geometric mean", 
"geometric mean (precision)"))

Value

• a list with the following components :
• testError
  the values of test error.
• avgError
  mean of test error.
• stdDev
  standard deviation of test error.
• metric
  values of the metric chosen.

Examples

## not run
# data(iris)
# Y <- iris$Species
# X <- iris[,-which(colnames(iris) == "Species")]

## 10-fold cross-validation for randomForest algorithm
## create the wrapper function
# genericAlgo <- function(X, Y) randomForest(X, Y)

## run 
# RF.10cv.iris <- generic.cv(X, Y, genericAlgo = genericAlgo, regression = FALSE)

## 10-fold cross-validation for Gradient Boosting Machines algorithm (gbm package)
## create the wrapper function

# require(gbm) || install.packages("gbm")
# genericAlgo <- function(X, Y) gbm.fit(X, Y, distribution = "multinomial", n.trees = 500,
# shrinkage = 0.05, interaction.depth = 24, n.minobsinnode = 1) 

## create a wrapper for the prediction function of gbm
# nClasses = length(unique(Y))
# specificPredictFunction <- function(model, newdata)
# {
#	modelPrediction = predict(model, newdata, 500) 
#	predictions = matrix(modelPrediction, ncol = nClasses )
#	colnames(predictions) = colnames(modelPrediction)
#	return(as.factor(apply(predictions, 1, function(Z) names(which.max(Z)))))
# }

## run
# gbm.10cv.iris <- generic.cv(X, Y, genericAlgo = genericAlgo, 
# specificPredictFunction = specificPredictFunction, regression = FALSE)