Weka_filters              package:RWeka              R Documentation

_R/_W_e_k_a _F_i_l_t_e_r_s

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

     R interfaces to Weka filters.

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

     Normalize(formula, data, subset, na.action, control = NULL)
     Discretize(formula, data, subset, na.action, control = NULL)

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

 formula: a symbolic description of a model. Note that for unsupervised
          filters the response can be omitted.

    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', or a character vector of
          control options, or 'NULL' (default). Available options can
          be obtained on-line using the Weka Option Wizard 'WOW', or
          the Weka documentation.

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

     'Normalize' implements an unsupervised filter that normalizes all
     instances of a dataset to have a given norm.  Only numeric values
     are  considered, and the class attribute is ignored.

     'Discretize' implements a supervised instance filter that
     discretizes a range of numeric attributes in the dataset into
     nominal attributes.  Discretization is by Fayyad & Irani's MDL
     method (the default).

     Note that these methods ignore nominal attributes, i.e., variables
     of class 'factor'.

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

     A data frame.

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

     U. M. Fayyad and K. B. Irani (1993). Multi-interval discretization
     of continuous-valued attributes for classification learning.
     _Thirteenth International Joint Conference on Artificial
     Intelligence_, 1022-1027. Morgan Kaufmann.

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

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

     ## Using a Weka data set ...
     w <- read.arff(system.file("arff","weather.arff",
                    package = "RWeka"))

     ## Normalize (response irrelevant)
     m1 <- Normalize(~., data = w)
     m1

     ## Discretize
     m2 <- Discretize(play ~., data = w)
     m2

