gettree

本程序是基于linux系统下c++代码

getTree             package:randomForest             R Documentation

_E_x_t_r_a_c_t _a _s_i_n_g_l_e _t_r_e_e _f_r_o_m _a _f_o_r_e_s_t.

_D_e_s_c_r_i_p_t_i_o_n:

     This function extract the structure of a tree from a
     'randomForest' object.

_U_s_a_g_e:

     getTree(rfobj, k=1, labelVar=FALSE)

_A_r_g_u_m_e_n_t_s:

   rfobj: a 'randomForest' object.

       k: which tree to extract?

labelVar: Should better labels be used for splitting variables and
          predicted class?

_D_e_t_a_i_l_s:

     For numerical predictors, data with values of the variable less
     than the splitting point go to the left daughter node.

     For categorical predictors, the splitting point is represented by
     an integer, whose binary expansion gives the identities of the
     categories that goes to left or right.  For example, if a
     predictor has three categories, and the split point is 5.  The
     binary expansion of 5 is (1, 0, 1) (because 5 = 1*2^0 + 0*2^1 +
     1*2^2), so cases with categories 1 or 3 in this predictor get sent
     to the left, and the rest to the right.

_V_a_l_u_e:

     A matrix (or data frame, if 'labelVar=TRUE') with six columns and
     number of rows equal to total number of nodes in the tree.  The
     six columns are:  

left daughter: the row where the left daughter node is; 0 if the node
          is terminal

right daughter: the row where the right daughter node is; 0 if the node
          is terminal

split var: which variable was used to split the node; 0 if the node is
          terminal

split point: where the best split is; see Details for categorical
          predictor

  status: is the node terminal (-1) or not (1)

prediction: the prediction for the node; 0 if the node is not terminal

_A_u_t_h_o_r(_s):

     Andy Liaw [e-mail]

_S_e_e _A_l_s_o:

     'randomForest'

_E_x_a_m_p_l_e_s:

     data(iris)
     ## Look at the third trees in the forest.
     getTree(randomForest(iris[,-5], iris[,5], ntree=10), 3, labelVar=TRUE)