train_model: makes a model object using the specified algorithm.
In RTextTools: Automatic Text Classification via Supervised Learning

Description Usage Arguments Details Value Author(s) Examples

Creates a trained model using the specified algorithm.

train_model(container, algorithm=c("SVM","SLDA","BOOSTING","BAGGING",
"RF","GLMNET","TREE","NNET"), 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, trace=FALSE, 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.
`algorithm`	Character vector (i.e. a string) specifying which algorithm to use. Use `print_algorithms` to see a list of options.
`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.
`trace`	Trace 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.
`...`	Additional arguments to be passed on to algorithm function calls.

Only one algorithm may be selected for training. See train_models and classify_models to train and classify using multiple algorithms.

Returns a trained model that can be subsequently used in classify_model to classify new data.

Timothy P. Jurka, Loren Collingwood

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)
rf_model <- train_model(container,"RF")
svm_model <- train_model(container,"SVM")