predict.randomforest

本程序是基于linux系统下c++代码

predict.randomForest      package:randomForest      R Documentation

_p_r_e_d_i_c_t _m_e_t_h_o_d _f_o_r _r_a_n_d_o_m _f_o_r_e_s_t _o_b_j_e_c_t_s

_D_e_s_c_r_i_p_t_i_o_n:

     Prediction of test data using random forest.

_U_s_a_g_e:

     ## S3 method for class 'randomForest':
     predict(object, newdata, type="response",
       norm.votes=TRUE, predict.all=FALSE, proximity=FALSE, nodes=FALSE,
       cutoff, ...)

_A_r_g_u_m_e_n_t_s:

  object: an object of class 'randomForest', as that created by the
          function 'randomForest'.

 newdata: a data frame or matrix containing new data.  (Note: If not
          given, the out-of-bag prediction in 'object' is returned.

    type: one of 'response', 'prob'. or 'votes', indicating the type of
          output: predicted values, matrix of class probabilities, or
          matrix of vote counts.  'class' is allowed, but automatically
          converted to "response", for backward compatibility.

norm.votes: Should the vote counts be normalized (i.e., expressed as
          fractions)?  Ignored if 'object$type' is 'regression'.

predict.all: Should the predictions of all trees be kept?

proximity: Should proximity measures be computed?  An error is issued
          if 'object$type' is 'regression'.

   nodes: Should the terminal node indicators (an n by ntree matrix) be
          return?  If so, it is in the ``nodes'' attribute of the
          returned object.

  cutoff: (Classification only)  A vector of length equal to number of
          classes.  The `winning' class for an observation is the one
          with the maximum ratio of proportion of votes to cutoff.
          Default is taken from the 'forest$cutoff' component of
          'object' (i.e., the setting used when running
          'randomForest').

     ...: not used currently.

_V_a_l_u_e:

     If 'object$type' is 'regression', a vector of predicted values is
     returned.  If 'predict.all=TRUE', then the returned object is a
     list of two components: 'aggregate', which is the vector of
     predicted values by the forest, and 'individual', which is a
     matrix where each column contains prediction by a tree in the
     forest. 

     If 'object$type' is 'classification', the object returned depends
     on the argument 'type': 

response: predicted classes (the classes with majority vote).

    prob: matrix of class probabilities (one column for each class and
          one row for each input).

    vote: matrix of vote counts (one column for each class and one row
          for each new input); either in raw counts or in fractions (if
          'norm.votes=TRUE').


     If 'predict.all=TRUE', then the 'individual' component of the
     returned object is a character matrix where each column contains
     the predicted class by a tree in the forest.

     If 'proximity=TRUE', the returned object is a list with two
     components: 'pred' is the prediction (as described above) and
     'proximity' is the proximitry matrix.  An error is issued if
     'object$type' is 'regression'.

     If 'nodes=TRUE', the returned object has a ``nodes'' attribute,
     which is an n by ntree matrix, each column containing the node
     number that the cases fall in for that tree.

_A_u_t_h_o_r(_s):

     Andy Liaw [e-mail] and Matthew Wiener
     [e-mail], based on original Fortran code by Leo
     Breiman and Adele Cutler.

_R_e_f_e_r_e_n_c_e_s:

     Breiman, L. (2001), _Random Forests_, Machine Learning 45(1),
     5-32.

_S_e_e _A_l_s_o:

     'randomForest'

_E_x_a_m_p_l_e_s:

     data(iris)
     set.seed(111)
     ind <- sample(2, nrow(iris), replace = TRUE, prob=c(0.8, 0.2))
     iris.rf <- randomForest(Species ~ ., data=iris[ind == 1,])
     iris.pred <- predict(iris.rf, iris[ind == 2,])
     table(observed = iris[ind==2, "Species"], predicted = iris.pred)