Difference between revisions of "Página de pruebas"
Adelo Vieira (talk | contribs) (Tag: Visual edit) |
Adelo Vieira (talk | contribs) (Tag: Visual edit) |
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| Line 34: | Line 34: | ||
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|Scaled linear discriminant analysis | |Scaled linear discriminant analysis | ||
| − | |Peters and Hothorn, 2012 | + | |[https://cran.r-project.org/web/packages/ipred/index.html Peters and Hothorn, 2012] |
| − | |ipred | + | |[https://cran.r-project.org/web/packages/ipred/index.html ipred] |
|SLDA | |SLDA | ||
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| Line 41: | Line 41: | ||
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|Bagging | |Bagging | ||
| − | |Peters and Hothorn, 2012 | + | |[https://cran.r-project.org/web/packages/ipred/index.html Peters and Hothorn, 2012] |
| − | |ipred | + | |[https://cran.r-project.org/web/packages/ipred/index.html ipred] |
|BAGGING | |BAGGING | ||
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| Line 48: | Line 48: | ||
|- | |- | ||
|Boosting | |Boosting | ||
| − | |Tuszynski, 2012 | + | |[https://cran.r-project.org/web/packages/caTools/index.html Tuszynski, 2012] |
| − | |caTools | + | |[https://cran.r-project.org/web/packages/caTools/index.html caTools] |
|BOOSTING | |BOOSTING | ||
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| Line 55: | Line 55: | ||
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|Random forest | |Random forest | ||
| − | |Liawand Wiener, 2002 | + | |[https://cran.r-project.org/web/packages/randomForest/index.html Liawand Wiener, 2002] |
| − | |randomForest | + | |[https://cran.r-project.org/web/packages/randomForest/index.html randomForest] |
|RF | |RF | ||
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| Line 63: | Line 63: | ||
|Neural networks | |Neural networks | ||
|Venables and Ripley, 2002 | |Venables and Ripley, 2002 | ||
| − | |nnet | + | |[https://cran.r-project.org/web/packages/nnet/index.html nnet] |
|NNET | |NNET | ||
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|Classification or regression tree | |Classification or regression tree | ||
|Ripley., 2012 | |Ripley., 2012 | ||
| − | |tree | + | |[https://cran.r-project.org/web/packages/tree/index.html tree] |
|TREE | |TREE | ||
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Revision as of 23:45, 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")