classifierOutput-class     package:MLInterfaces     R Documentation

_C_l_a_s_s "_c_l_a_s_s_i_f_i_e_r_O_u_t_p_u_t"

_D_e_s_c_r_i_p_t_i_o_n:

     This class summarizes the output values from different
     classifiers.

_O_b_j_e_c_t_s _f_r_o_m _t_h_e _C_l_a_s_s:

     Objects are typically created during the application of a
     supervised  machine learning algorithm to data and are the value
     returned.  It is very unlikely that any user would create such an
     object by hand.

_S_l_o_t_s:


     '_t_e_s_t_O_u_t_c_o_m_e_s': Object of class '"factor"' that lists the actual
          outcomes in the records on the test set 

     '_t_e_s_t_P_r_e_d_i_c_t_i_o_n_s': Object of class '"factor"' that lists the
          predictions of outcomes in the test set 

     '_t_e_s_t_S_c_o_r_e_s': Object of class '"ANY"' - this element will include
          matrices or vectors or arrays that include information that
          is typically related to the posterior probability of
          occupancy of the predicted class or of all classes.  The
          actual contents of this slot can be determined by inspecting
          the converter element of the learnerSchema used to select the
          model. 

     '_t_r_a_i_n_O_u_t_c_o_m_e_s': Object of class '"factor"' that lists the actual
          outcomes in records on the training set 

     '_t_r_a_i_n_P_r_e_d_i_c_t_i_o_n_s': Object of class '"factor"' that lists the
          predicted outcomes in the training set 

     '_t_r_a_i_n_S_c_o_r_e_s': Object of class '"ANY"' see the description of
          'testScores' above; the same information is returned, but
          applicable to the training set records.

     '_R_O_b_j_e_c_t': Object of class '"ANY"' - when the 'trainInd' parameter
          of the 'MLearn' call is numeric, this slot holds the return
          value of the underlying R function that carried out the
          predictive modeling.  For example, if 'rpartI' was used as
          MLearn 'method', 'Robject' holds an instance of the 'rpart'
          S3 class, and 'plot' and 'text' methods can be applied to
          this.  When the 'trainInd' parameter of the 'MLearn' call is
          an instance of 'xvalSpec', this slot holds a 'list' of
          results of cross-validatory iterations.  Each element of this
          list has two elements: 'test.idx', giving the numeric indices
          of the test cases for the associated cross-validation
          iteration, and 'mlans', which is the 'classifierOutput' for
          the associated iteration.  See the example for an
          illustration of 'digging out' the predicted probabilities
          associated with each cross-validation iteration executed
          through an xvalSpec specification. 

     '_c_a_l_l': Object of class '"call"' - records the call used to
          generate the classifierOutput RObject 

_M_e_t_h_o_d_s:


     _c_o_n_f_u_M_a_t 'signature(obj = "classifierOutput")': Compute the
          confusion matrix for test records. 

     _c_o_n_f_u_M_a_t_T_r_a_i_n 'signature(obj = "classifierOutput")': Compute the
          confusion matrix for training set.  Typically yields
          optimistically biased information on misclassification rate. 

     _R_O_b_j_e_c_t 'signature(obj = "classifierOutput")': The R object
          returned by the underlying classifier. This can then be
          passed on to specific methods for those objects, when they
          exist. 

     _s_h_o_w 'signature(object = "classifierOutput")': A print method that
          provides a summary of the output of the classifier. 

     _t_e_s_t_S_c_o_r_e_s 'signature(object = "classifierOutput")': ... 

     _t_e_s_t_P_r_e_d_i_c_t_i_o_n_s 'signature(object = "classifierOutput")': Print
          the predicted classes for each sample/individual in the test
          set. 

     _t_r_a_i_n_P_r_e_d_i_c_t_i_o_n_s 'signature(object = "classifierOutput")': Print
          the predicted classes for each sample/individual in the
          training set. 

     _f_s_H_i_s_t_o_r_y 'signature(object = "classifierOutput")': ... 

_A_u_t_h_o_r(_s):

     V. Carey

_E_x_a_m_p_l_e_s:

     showClass("classifierOutput")
     library(golubEsets)
     data(Golub_Train) # now cross-validate a neural net
     set.seed(1234)
     xv5 = xvalSpec("LOG", 5, balKfold.xvspec(5))
     m2 = MLearn(ALL.AML~., Golub_Train[1000:1050,], nnetI, xv5, 
        size=5, decay=.01, maxit=1900 )
     testScores(RObject(m2)[[1]]$mlans)
     alls = lapply(RObject(m2), function(x) testScores(x$mlans))

