audit                 package:rattle                 R Documentation

_S_a_m_p_l_e _d_a_t_a_s_e_t _f_o_r _i_l_l_u_s_t_r_a_t_i_o_n _R_a_t_t_l_e _f_u_n_c_t_i_o_n_a_l_i_t_y.

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

     The audit dataset is an artificially constructed dataset that has
     some of the characteristics of a true audit dataset for modelling
     productive and non-productive audits. It is used to illustrate
     binary classification.

     The target variable is 'Adjusted', an integer which is either 0
     (for non-producitve audits) or 1 (for productive audits).
     Productive audits are those that result in an adjustment being
     made to a client's claims. The dollar value of those adjustments
     is also recorded (as the 'Adjusted' column).

     The independent variables include 'Age', type of 'Employment',
     level of 'Education', 'Marital' status, 'Occupation', level of
     'Income', 'Sex', amount of 'Deductions' being claimed, 'Hours'
     worked per week, and country in which they have a bank 'Account'.

     An identifier is included as the 'ID' variable.

     The dataset is quite small, consisting of just 2000 entities. It
     primary purpose is to illustrate modelling in Rattle, so a
     minimally sized dataset is suitable.

_F_o_r_m_a_t:

     A data frame.

