confuMat-methods        package:MLInterfaces        R Documentation

_C_o_m_p_u_t_e _t_h_e _c_o_n_f_u_s_i_o_n _m_a_t_r_i_x _f_o_r _a _c_l_a_s_s_i_f_i_e_r.

_D_e_s_c_r_i_p_t_i_o_n:

     This function will compute the confusion matrix for a classifier's
     output

_M_e_t_h_o_d_s:


     _o_b_j = "_c_l_a_s_s_i_f_O_u_t_p_u_t" Typically, an instance of class 
          '"classifierOutput"' is built on a training subset of the
          input data. The model is then used to predict the class of
          samples in the test set.  When the true class labels for the
          test set are available the confusion matrix is the
          cross-tabulation of the true labels of the test set against
          the predictions from the classifier. 

     _o_b_j = "_c_l_a_s_s_i_f_i_e_r_O_u_t_p_u_t", _t_y_p_e="_c_h_a_r_a_c_t_e_r" For instances of
          classifierOutput, it is possible to specify the 'type' of
          confusion matrix desired. The default is 'test', which
          tabulates classes from the test set against the associated
          predictions.  If 'type' is 'train', the training class vector
          is tabulated against the predictions on the training set. 

_E_x_a_m_p_l_e_s:

     library(golubEsets)
     data(Golub_Merge)
     smallG <- Golub_Merge[101:150,]
     k1 <- MLearn(ALL.AML~., smallG, knnI(k=1), 1:30)
     confuMat(k1)
     confuMat(k1, "train")

