Examples of LogisticBase

weka.classifiers.trees.lmt.LogisticBase
Base/helper class for building logistic regression models with the LogitBoost algorithm. Used for building logistic model trees (weka.classifiers.trees.lmt.LMT) and standalone logistic regression (weka.classifiers.functions.SimpleLogistic). Valid options are:
```
 -D If set, classifier is run in debug mode and may output additional info to the console
```
@author Niels Landwehr @author Marc Sumner @version $Revision: 1.9 $

Examples of weka.classifiers.trees.lmt.LogisticBase

  m_NominalToBinary = new NominalToBinary();
  m_NominalToBinary.setInputFormat(data);
  data = Filter.useFilter(data, m_NominalToBinary);
  
  //create actual logistic model
  m_boostedModel = new LogisticBase(m_numBoostingIterations, m_useCrossValidation, m_errorOnProbabilities);
  m_boostedModel.setMaxIterations(m_maxBoostingIterations);
  m_boostedModel.setHeuristicStop(m_heuristicStop);
        m_boostedModel.setWeightTrimBeta(m_weightTrimBeta);
        m_boostedModel.setUseAIC(m_useAIC);

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Examples of weka.classifiers.trees.lmt.LogisticBase

  m_NominalToBinary = new NominalToBinary();
  m_NominalToBinary.setInputFormat(data);
  data = Filter.useFilter(data, m_NominalToBinary);
  
  //create actual logistic model
  m_boostedModel = new LogisticBase(m_numBoostingIterations, m_useCrossValidation, m_errorOnProbabilities);
  m_boostedModel.setMaxIterations(m_maxBoostingIterations);
  m_boostedModel.setHeuristicStop(m_heuristicStop);
        m_boostedModel.setWeightTrimBeta(m_weightTrimBeta);
        m_boostedModel.setUseAIC(m_useAIC);

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Examples of weka.classifiers.trees.lmt.LogisticBase

  m_NominalToBinary = new NominalToBinary();
  m_NominalToBinary.setInputFormat(data);
  data = Filter.useFilter(data, m_NominalToBinary);
  
  //create actual logistic model
  m_boostedModel = new LogisticBase(m_numBoostingIterations, m_useCrossValidation, m_errorOnProbabilities);
  m_boostedModel.setMaxIterations(m_maxBoostingIterations);
  m_boostedModel.setHeuristicStop(m_heuristicStop);
        m_boostedModel.setWeightTrimBeta(m_weightTrimBeta);
        m_boostedModel.setUseAIC(m_useAIC);

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