Examples of NominalToBinary

weka.filters.supervised.attribute.NominalToBinary

Converts all nominal attributes into binary numeric attributes. An attribute with k values is transformed into k binary attributes if the class is nominal (using the one-attribute-per-value approach). Binary attributes are left binary, if option '-A' is not given.If the class is numeric, k - 1 new binary attributes are generated in the manner described in "Classification and Regression Trees" by Breiman et al. (i.e. taking the average class value associated with each attribute value into account)

For more information, see:

L. Breiman, J.H. Friedman, R.A. Olshen, C.J. Stone (1984). Classification and Regression Trees. Wadsworth Inc.

BibTeX:

 @book{Breiman1984, author = {L. Breiman and J.H. Friedman and R.A. Olshen and C.J. Stone}, publisher = {Wadsworth Inc}, title = {Classification and Regression Trees}, year = {1984}, ISBN = {0412048418} }

Valid options are:

 -N Sets if binary attributes are to be coded as nominal ones.

 -A For each nominal value a new attribute is created,  not only if there are more than 2 values.

@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5541 $

weka.filters.unsupervised.attribute.NominalToBinary

Valid options are:

 -N Sets if binary attributes are to be coded as nominal ones.

 -A For each nominal value a new attribute is created,  not only if there are more than 2 values.

 -R <col1,col2-col4,...> Specifies list of columns to act on. First and last are  valid indexes. (default: first-last)

 -V Invert matching sense of column indexes.

@author Eibe Frank (eibe@cs.waikato.ac.nz) @version $Revision: 5543 $

  m_ReplaceMissingValues = new ReplaceMissingValues();
  m_ReplaceMissingValues.setInputFormat(data);
  data = Filter.useFilter(data, m_ReplaceMissingValues);
  
  //convert nominal attributes
  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);

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Examples of NominalToBinary

Examples of weka.filters.unsupervised.attribute.NominalToBinary