RandomForest-class           package:party           R Documentation

_C_l_a_s_s "_R_a_n_d_o_m_F_o_r_e_s_t"

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

     A class for representing random forest ensembles.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects can be created by calls of the form 'new("RandomForest",
     ...)'.

_S_l_o_t_s:

     '_e_n_s_e_m_b_l_e': Object of class '"list"', each element being an object
          of class 'BinaryTree-class'.

     '_d_a_t_a': an object of class 'ModelEnv'.

     '_r_e_s_p_o_n_s_e_s': an object of class '"VariableFrame"' storing the
          values of the response variable(s). 

     '_c_o_n_d__d_i_s_t_r__r_e_s_p_o_n_s_e': a function computing the conditional
          distribution of the response. 

     '_p_r_e_d_i_c_t__r_e_s_p_o_n_s_e': a function for computing predictions. 

     '_p_r_e_d_i_c_t_i_o_n__w_e_i_g_h_t_s': a function for extracting weights from
          terminal nodes. 

_M_e_t_h_o_d_s:

     _t_r_e_e_r_e_s_p_o_n_s_e 'signature(object = "RandomForest")': ... 

     _w_e_i_g_h_t_s 'signature(object = "RandomForest")': ... 

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

         ### honest (i.e., out-of-bag) cross-classification of 
         ### true vs. predicted classes
         table(mammoexp$ME, predict(cforest(ME ~ ., data = mammoexp, 
                                    control = cforest_control(ntree = 50)), 
                                    OOB = TRUE))

