cross_validate: used for cross-validation of various algorithms.
In RTextTools: Automatic Text Classification via Supervised Learning

Description Usage Arguments Author(s) Examples

Performs n-fold cross-validation of specified algorithm.

cross_validate(container, nfold, algorithm = c("SVM", "SLDA", "BOOSTING", 
"BAGGING", "RF", "GLMNET", "TREE", "NNET"), seed = NA, 
method = "C-classification", cross = 0, cost = 100, kernel = "radial", 
maxitboost = 100, maxitglm = 10^5, size = 1, maxitnnet = 1000, MaxNWts = 10000, 
rang = 0.1, decay = 5e-04, ntree = 200, l1_regularizer = 0, l2_regularizer = 0, 
use_sgd = FALSE, set_heldout = 0, verbose = FALSE)

`container`	Class of type `matrix_container-class` generated by the `create_container` function.
`nfold`	Number of folds to perform for cross-validation.
`algorithm`	A string specifying which algorithm to use. Use `print_algorithms` to see a list of options.
`seed`	Random seed number used to replicate cross-validation results.
`method`	Method parameter for SVM implentation. See e1071 documentation for more details.
`cross`	Cross parameter for SVM implentation. See e1071 documentation for more details.
`cost`	Cost parameter for SVM implentation. See e1071 documentation for more details.
`kernel`	Kernel parameter for SVM implentation. See e1071 documentation for more details.
`maxitboost`	Maximum iterations parameter for boosting implentation. See caTools documentation for more details.
`maxitglm`	Maximum iterations parameter for glmnet implentation. See glmnet documentation for more details.
`size`	Size parameter for neural networks implentation. See nnet documentation for more details.
`maxitnnet`	Maximum iterations for neural networks implentation. See nnet documentation for more details.
`MaxNWts`	Maximum number of weights parameter for neural networks implentation. See nnet documentation for more details.
`rang`	Range parameter for neural networks implentation. See nnet documentation for more details.
`decay`	Decay parameter for neural networks implentation. See nnet documentation for more details.
`ntree`	Number of trees parameter for RandomForests implentation. See randomForest documentation for more details.
`l1_regularizer`	An `numeric` turning on L1 regularization and setting the regularization parameter. A value of 0 will disable L1 regularization. See maxent documentation for more details.
`l2_regularizer`	An `numeric` turning on L2 regularization and setting the regularization parameter. A value of 0 will disable L2 regularization. See maxent documentation for more details.
`use_sgd`	A `logical` indicating that SGD parameter estimation should be used. Defaults to `FALSE`. See maxent documentation for more details.
`set_heldout`	An `integer` specifying the number of documents to hold out. Sets a held-out subset of your data to test against and prevent overfitting. See maxent documentation for more details.
`verbose`	A `logical` specifying whether to provide descriptive output about the training process. Defaults to `FALSE`, or no output. See maxent documentation for more details.

Loren Collingwood, Timothy P. Jurka

library(RTextTools)
data(NYTimes)
data <- NYTimes[sample(1:3100,size=100,replace=FALSE),]
matrix <- create_matrix(cbind(data["Title"],data["Subject"]), language="english", 
removeNumbers=TRUE, stemWords=FALSE, weighting=tm::weightTfIdf)
container <- create_container(matrix,data$Topic.Code,trainSize=1:75, testSize=76:100, 
virgin=FALSE)
svm <- cross_validate(container,2,algorithm="SVM")

Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve

Fold 1 Out of Sample Accuracy = 0.2083333
Fold 2 Out of Sample Accuracy = 0.1923077