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().'''''
  
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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:
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{| class="wikitable"
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|+
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!Algorithms
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!Author
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!From package
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!Keyword
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!Comment
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!
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|-
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|Support vector machine
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|[https://cran.r-project.org/web/packages/e1071/index.html Meyer et al., 2012]
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|
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|SVM
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|Low-memory algorithms
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|
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|-
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|Glmnet
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|Friedman et al., 2010
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|
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|GLMNET
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|Low-memory algorithms
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|
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|-
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|Maximum entropy
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|Jurka, 2012
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|
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|MAXENT
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|Low-memory algorithms
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|
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|-
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|Scaled linear discriminant analysis
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|Peters and Hothorn, 2012
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|ipred
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|SLDA
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|
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|
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|-
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|Bagging
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|Peters and Hothorn, 2012
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|ipred
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|BAGGING
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|
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|
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|-
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|Boosting
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|Tuszynski, 2012
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|caTools
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|BOOSTING
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|
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|
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|-
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|Random forest
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|Liawand Wiener, 2002
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|randomForest
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|RF
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|
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|
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|-
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|Neural networks
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|Venables and Ripley, 2002
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|nnet
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|NNET
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|
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|
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|-
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|Classification or regression tree
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|Ripley., 2012
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|tree
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|TREE
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|
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|
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|}
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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")