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Revision as of 23:15, 6 April 2019 by Adelo Vieira (talk | contribs)
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 | Comment | |
|---|---|---|---|---|---|
| Support vector machine | Meyer et al., 2012 | SVM | Low-memory algorithms | ||
| Glmnet | Friedman et al., 2010 | GLMNET | Low-memory algorithms | ||
| Maximum entropy | Jurka, 2012 | MAXENT | Low-memory algorithms | ||
| Scaled linear discriminant analysis | Peters and Hothorn, 2012 | ipred | SLDA | ||
| Bagging | Peters and Hothorn, 2012 | ipred | BAGGING | ||
| Boosting | Tuszynski, 2012 | caTools | BOOSTING | ||
| Random forest | Liawand Wiener, 2002 | randomForest | RF | ||
| Neural networks | Venables and Ripley, 2002 | nnet | NNET | ||
| Classification or regression tree | Ripley., 2012 | tree | TREE |
GLMNET <- train_model(container,"GLMNET")