Name

adams.flow.transformer.MekaTrainClassifier

Synopsis

Trains a Meka classifier based on the incoming dataset and outputs the built classifier alongside the training header (in a model container).

Additional information

Flow input/output:
- input: weka.core.Instances
- output: adams.flow.container.WekaModelContainer

Container information:
- adams.flow.container.WekaModelContainer:
   - Model: model object; java.lang.Object
   - Header: dataset header; weka.core.Instances
   - Dataset: full dataset; weka.core.Instances

Options

• loggingLevel
  

  The logging level for outputting errors and debugging output.

  command-line	-logging-level <OFF|SEVERE|WARNING|INFO|CONFIG|FINE|FINER|FINEST>
  default	WARNING
  min-user-mode	Expert

  
  
• name
  

  The name of the actor.

  command-line	-name <java.lang.String>
  default	MekaTrainClassifier

  
  
• annotations
  

  The annotations to attach to this actor.

  command-line	-annotation <adams.core.base.BaseAnnotation>
  default

  
  
• skip
  

  If set to true, transformation is skipped and the input token is just forwarded as it is.

  command-line	-skip <boolean>
  default	false

  
  
• stopFlowOnError
  

  If set to true, the flow execution at this level gets stopped in case this actor encounters an error; the error gets propagated; useful for critical actors.

  command-line	-stop-flow-on-error <boolean>
  default	false
  min-user-mode	Expert

  
  
• silent
  

  If enabled, then no errors are output in the console; Note: the enclosing actor handler must have this enabled as well.

  command-line	-silent <boolean>
  default	false
  min-user-mode	Expert

  
  
• classifier
  

  The MEKA classifier to train on the input data.

  command-line	-classifier <adams.flow.core.CallableActorReference>
  default	MekaClassifierSetup