Difference between revisions of "Página de pruebas"
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!Fake news challenge dataset | !Fake news challenge dataset | ||
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| − | |Página de pruebas#General linearized models | + | |[[Página de pruebas#General linearized models|General linearized models]] |
|Friedman et al., 2010 | |Friedman et al., 2010 | ||
|[https://cran.r-project.org/web/packages/glmnet/index.html glmnet] | |[https://cran.r-project.org/web/packages/glmnet/index.html glmnet] | ||
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| − | |Support vector machine | + | |[[Página de pruebas#Support vector machine|Support vector machine]] |
|Meyer et al., 2012 | |Meyer et al., 2012 | ||
|[https://cran.r-project.org/web/packages/e1071/index.html e1071] | |[https://cran.r-project.org/web/packages/e1071/index.html e1071] | ||
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| − | |Maximum entropy | + | |[[Página de pruebas#Maximum entropy|Maximum entropy]] |
|Jurka, 2012 | |Jurka, 2012 | ||
|[https://cran.r-project.org/web/packages/maxent/index.html maxent] | |[https://cran.r-project.org/web/packages/maxent/index.html maxent] | ||
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| − | |Classification or regression tree | + | |[[Página de pruebas#Classification or regression tree|Classification or regression tree]] |
|Ripley., 2012 | |Ripley., 2012 | ||
|[https://cran.r-project.org/web/packages/tree/index.html tree] | |[https://cran.r-project.org/web/packages/tree/index.html tree] | ||
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| − | |Random forest | + | |[[Página de pruebas#Random forest|Random forest]] |
|Liawand Wiener, 2002 | |Liawand Wiener, 2002 | ||
|[https://cran.r-project.org/web/packages/randomForest/index.html randomForest] | |[https://cran.r-project.org/web/packages/randomForest/index.html randomForest] | ||
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| − | |Boosting | + | |[[Página de pruebas#Boosting|Boosting]] |
|Tuszynski, 2012 | |Tuszynski, 2012 | ||
|[https://cran.r-project.org/web/packages/caTools/index.html caTools] | |[https://cran.r-project.org/web/packages/caTools/index.html caTools] | ||
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| − | |Neural networks | + | |[[Página de pruebas#Neural networks|Neural networks]] |
|Venables and Ripley, 2002 | |Venables and Ripley, 2002 | ||
|[https://cran.r-project.org/web/packages/nnet/index.html nnet] | |[https://cran.r-project.org/web/packages/nnet/index.html nnet] | ||
Revision as of 01:05, 7 April 2019
Contents
RTextTools - A Supervised LearningPackage for Text Classification
https://journal.r-project.org/archive/2013/RJ-2013-001/RJ-2013-001.pdf
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
