Weka_classifier_lazy          package:RWeka          R Documentation

_R/_W_e_k_a _L_a_z_y _L_e_a_r_n_e_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     R interfaces to Weka lazy learners.

_U_s_a_g_e:

     IBk(formula, data, subset, na.action,
         control = Weka_control(), options = NULL)
     LBR(formula, data, subset, na.action,
         control = Weka_control(), options = NULL)

_A_r_g_u_m_e_n_t_s:

 formula: a symbolic description of the model to be fit.

    data: an optional data frame containing the variables in the model.

  subset: an optional vector specifying a subset of observations to be
          used in the fitting process.

na.action: a function which indicates what should happen when the data
          contain 'NA's.

 control: an object of class 'Weka_control' giving options to be passed
          to the Weka learner.  Available options can be obtained
          on-line using the Weka Option Wizard 'WOW', or the Weka
          documentation.

 options: a named list of further options, or 'NULL' (default).  See
          *Details*.

_D_e_t_a_i_l_s:

     There are a 'predict' method for predicting from the fitted
     models, and a 'summary' method based on
     'evaluate_Weka_classifier'.

     'IBk' provides a k-nearest neighbors classifier, see Aha & Kibler
     (1991).

     'LBR' (Lazy Bayesian Rules) implements a lazy learning approach
     to lessening the attribute-independence assumption of naive Bayes
     as suggested by Zheng & Webb (2000).

     The model formulae should only use the '+' and '-' operators to
     indicate the variables to be included or not used, respectively.

     Argument 'options' allows further customization.  Currently,
     options 'model' and 'instances' (or partial matches for these) are
     used: if set to 'TRUE', the model frame or the corresponding Weka
     instances, respectively, are included in the fitted model object,
     possibly speeding up subsequent computations on the object.  By
     default, neither is included.

_V_a_l_u_e:

     A list inheriting from classes 'Weka_lazy' and 'Weka_classifiers'
     with components including 

classifier: a reference (of class 'jobjRef') to a Java object obtained
          by applying the Weka 'buildClassifier' method to build the
          specified model using the given control options.

predictions: a numeric vector or factor with the model predictions for
          the training instances (the results of calling the Weka
          'classifyInstance' method for the built classifier and each
          instance).

    call: the matched call.

_R_e_f_e_r_e_n_c_e_s:

     D. Aha and D. Kibler (1991). Instance-based learning algorithms.
     _Machine Learning_, *6*, 37-66.

     Z. Zheng and G. Webb (2000). Lazy learning of Bayesian rules.
     _Machine Learning_, *41*/1, 53-84.

_S_e_e _A_l_s_o:

     Weka_classifiers

