Página de pruebas

RTextTools - A Supervised LearningPackage for Text Classification

https://journal.r-project.org/archive/2013/RJ-2013-001/RJ-2013-001.pdf

http://www.rtexttools.com/

https://cran.r-project.org/web/packages/RTextTools/index.html

The train_model() function takes each algorithm, one by one, to produce an object passable to classify_model().

A convenience train_models() function trains all models at once by passing in a vector of model requests. The syntax below demonstrates model creation for all nine algorithms:

Algorithms	Author	From package	Keyword	Accuracy
				Fake news dataset	Fake news challenge dataset
General linearized models	Friedman et al., 2010	glmnet	GLMNET*
Support vector machine	Meyer et al., 2012	e1071	SVM*
Maximum entropy	Jurka, 2012	maxent	MAXENT*
Classification or regression tree	Ripley., 2012	tree	TREE
Random forest	Liawand Wiener, 2002	randomForest	RF
Boosting	Tuszynski, 2012	caTools	BOOSTING
Neural networks	Venables and Ripley, 2002	nnet	NNET
Bagging	Peters and Hothorn, 2012	ipred	BAGGING**
Scaled linear discriminant analysis	Peters and Hothorn, 2012	ipred	SLDA**
* Low-memory algorithm ** Very high-memory algorithm

GLMNET <- train_model(container,"GLMNET")

General linearized models

Support vector machine

Maximum entropy

Classification or regression tree

Random forest

Boosting

Neural networks