getAucEstimate           package:gputools           R Documentation

_E_s_t_i_m_a_t_e _t_h_e _A_U_C _o_f _t_h_e _R_O_C

_D_e_s_c_r_i_p_t_i_o_n:

     This function give a quick estimate of the area under the curve
     (AUC) of the receiver operating characteristic (ROC).  It is a
     quick way to get some hint at how good a binary classifier is at
     classifying.  The algorithm is based on a paper by David Hand and
     Robert Till (see references).

_U_s_a_g_e:

     getAucEstimate(classes, scores)

_A_r_g_u_m_e_n_t_s:

 classes: a vector of floating point numbers.  Each entry i corresponds
          to the real class of a point and should be either 0 or 1. 
          The negative class is represented by 0 and the positive class
          by 1. These entries correspond both in number and order to
          the same points associated with the scores vector. 

  scores: a vector of floating point numbers.  Each entry i corresponds
          to the probability that a point is in the positive class of a
          binary classification.  This will be the output of, for
          example, a binary classifier based on logistic regression. 
          These entries should correspond both in number and order to
          the same points associated with the classes vector. 

_V_a_l_u_e:

     a single floating point number of double precision.  This number
     represents an  estimate of the auc score for the algorithm
     responsible for the scores vector. The estimation is according to
     the method of David Hand and Robert Till (see references).

_R_e_f_e_r_e_n_c_e_s:

     Hand, David J. and Till, Robert J. (2001). A simple generalisation
     of the area under the ROC curve for multiple class classification
     problems. _Machine Learning_. 45, 171-186.

_E_x_a_m_p_l_e_s:

     # generate some fake data
     classes <- round(runif(20, min = 0, max = 1))

     # fake probability that point i is in the positive class
     scores <- runif(20, min = 0, max = 1)

     b <- getAucEstimate(classes, scores)
     print(b)

