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 
          '"classifOutput"' 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")

