Weka_classifiers            package:RWeka            R Documentation

_R/_W_e_k_a _C_l_a_s_s_i_f_i_e_r_s

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

     R interfaces to Weka classifiers.

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

     Supervised learners, i.e., algorithms for classification and
     regression, are termed classifiers by Weka.  (Numeric
     prediction, i.e., regression, is interpreted as prediction of a
     continuous class.)

     R interface functions to Weka classifiers are created by
     'make_Weka_classifier', and have formals 'formula', 'data',
     'subset', 'na.action', and 'control' (default: none), where the
     first four have the usual meanings for statistical modeling
     functions in R, and the last again specifies the control options
     to be employed by the Weka learner.  By default, the model
     formulae should only use the '+' and '-' operators to indicate the
     variables to be included or not used, respectively.

     Objects created by these interfaces always inherit from class
     'Weka_classifier', and have at least suitable 'print', 'summary'
     (via 'evaluate_Weka_classifier'), and 'predict' methods.

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

     Available standard interface functions are documented in
     Weka_classifier_functions (regression and classification function
     learners), Weka_classifier_lazy (lazy learners),
     Weka_classifier_meta (meta learners), Weka_classifier_rules (rule
     learners), and Weka_classifier_trees (regression and
     classification tree learners).

