Weka_classifier_rules         package:RWeka         R Documentation

_R/_W_e_k_a _R_u_l_e _L_e_a_r_n_e_r_s

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

     R interfaces to Weka rule learners.

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

     JRip(formula, data, subset, na.action,
          control = Weka_control(), options = NULL)
     M5Rules(formula, data, subset, na.action,
             control = Weka_control(), options = NULL)
     OneR(formula, data, subset, na.action,
          control = Weka_control(), options = NULL)
     PART(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'.

     'JRip' implements a propositional rule learner, Repeated
     Incremental Pruning to Produce Error Reduction (RIPPER), as
     proposed by Cohen (1995).

     'M5Rules' generates a decision list for regression problems using
     separate-and-conquer.  In each iteration it builds an model tree
     using M5 and makes the best leaf into a rule.  See Hall, Holmes
     and Frank (1999) for more information.

     'OneR' builds a simple 1-R classifier, see Holte (1993).

     'PART' generates PART decision lists using the approach of Frank
     and Witten (1998).

     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_rules' 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:

     W. W. Cohen (1995). Fast effective rule induction. In A. Prieditis
     and S. Russell (eds.), _Proceedings of the 12th International
     Conference on Machine Learning_, pages 115-123. Morgan Kaufmann.
     ISBN 1-55860-377-8. <URL:
     http://citeseer.ist.psu.edu/cohen95fast.html>

     E. Frank and I. H. Witten (1998). Generating accurate rule sets
     without global optimization. In J. Shavlik (ed.), _Machine
     Learning: Proceedings of the Fifteenth International Conference_.
     Morgan Kaufmann Publishers: San Francisco, CA. <URL:
     http://www.cs.waikato.ac.nz/~eibe/pubs/ML98-57.ps.gz>

     M. Hall, G. Holmes, and E. Frank (1999). Generating rule sets from
     model trees. _Proceedings of the Twelfth Australian Joint
     Conference on Artificial Intelligence_, Sydney, Australia, pages
     1-12. Springer-Verlag. <URL:
     http://citeseer.ist.psu.edu/holmes99generating.html>

     R. C. Holte (1993). Very simple classification rules perform well
     on most commonly used datasets. _Machine Learning_, *11*, 63-91. 

     I. H. Witten and E. Frank (2005). _Data Mining: Practical Machine
     Learning Tools and Techniques_. 2nd Edition, Morgan Kaufmann, San
     Francisco.

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

     Weka_classifiers

_E_x_a_m_p_l_e_s:

     M5Rules(mpg ~ ., data = mtcars)

     m <- PART(Species ~ ., data = iris)
     m
     summary(m)

