Weka_classifier_meta          package:RWeka          R Documentation

_R/_W_e_k_a _M_e_t_a _L_e_a_r_n_e_r_s

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

     R interfaces to Weka meta learners.

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

     AdaBoostM1(formula, data, subset, na.action,
                control = Weka_control(), options = NULL)
     Bagging(formula, data, subset, na.action,
             control = Weka_control(), options = NULL)
     LogitBoost(formula, data, subset, na.action,
                control = Weka_control(), options = NULL)
     MultiBoostAB(formula, data, subset, na.action,
                  control = Weka_control(), options = NULL)
     Stacking(formula, data, subset, na.action,
              control = Weka_control(), options = NULL)
     CostSensitiveClassifier(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.  Base classifiers with an available R/Weka
          interface (see 'list_Weka_interfaces'), can be specified
          (using the 'W' option) via their base name as shown in the
          interface registry (see the examples), or their interface
          function.

 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'.

     'AdaBoostM1' implements the AdaBoost M1 method of Freund and
     Schapire (1996).

     'Bagging' provides bagging (Breiman, 1996).

     'LogitBoost' performs boosting via additive logistic regression
     (Friedman, Hastie and Tibshirani, 2000).

     'MultiBoostAB' implements MultiBoosting (Webb, 2000), an extension
     to the AdaBoost technique for forming decision committees which
     can be viewed as a combination of AdaBoost and wagging.

     'Stacking' provides stacking (Wolpert, 1992).

     'CostSensitiveClassifier' makes its base classifier
     cost-sensitive.

     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_meta' 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:

     L. Breiman (1996). Bagging predictors. _Machine Learning_, *24*/2,
     123-140.

     Y. Freund and R. E. Schapire (1996). Experiments with a new
     boosting algorithm. In _Proceedings of the International
     Conference on Machine Learning_, pages 148-156. Morgan Kaufmann:
     San Francisco.

     J. H. Friedman, T. Hastie, and R. Tibshirani (2000). Additive
     logistic regression: A statistical view of boosting. _Annals of
     Statistics_, *28*/2, 337-374.

     G. I. Webb (2000). MultiBoosting: A technique for combining
     boosting and wagging. _Machine Learning_, *40*/2, 159-196.

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

     D. H. Wolpert (1992). Stacked generalization. _Neural Networks_,
     *5*, 241-259.

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

     Weka_classifiers

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

     ## Use AdaBoostM1 with decision stumps.
     m1 <- AdaBoostM1(Species ~ ., data = iris,
                      control = Weka_control(W = "DecisionStump"))
     table(predict(m1), iris$Species)

     summary(m1) # uses evaluate_Weka_classifier()

     ## Control options for the base classifiers employed by the meta
     ## learners (apart from Stacking) can be given as follows:
     m2 <- AdaBoostM1(Species ~ ., data = iris,
                      control = Weka_control(W = list(J48, M = 30)))

