Difference between revisions of "Página de pruebas"
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| + | 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: | ||
| + | {| class="wikitable" | ||
| + | |+ | ||
| + | !Algorithms | ||
| + | !Author | ||
| + | !From package | ||
| + | !Keyword | ||
| + | !Comment | ||
| + | ! | ||
| + | |- | ||
| + | |Support vector machine | ||
| + | |[https://cran.r-project.org/web/packages/e1071/index.html 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") | ||
Revision as of 23:15, 6 April 2019
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")