mlbench.friedman2          package:mlbench          R Documentation

_B_e_n_c_h_m_a_r_k _P_r_o_b_l_e_m _F_r_i_e_d_m_a_n _2

_D_e_s_c_r_i_p_t_i_o_n:

     The regression problem Friedman 2 as described in Friedman (1991)
     and Breiman (1996). Inputs are 4 independent variables uniformly
     distrtibuted over the ranges

                            0 <= x1 <= 100


                        40 pi <= x2 <= 560 pi


                             0 <= x3 <= 1


                            1 <= x4 <= 11


     The outputs are created according to the formula

            y = (x1^2 + (x2 x3 - (1/(x2 x4)))^2)^{0.5} + e

     where e is N(0,sd).

_U_s_a_g_e:

     mlbench.friedman2(n, sd=125)

_A_r_g_u_m_e_n_t_s:

       n: number of patterns to create

      sd: Standard deviation of noise. The default value of 125 gives a
          signal to noise ratio (i.e., the ratio of the standard
          deviations) of 3:1. Thus, the variance of the function itself
          (without noise) accounts for 90% of the total variance.

_V_a_l_u_e:

     Returns a list with components 

       x: input values (independent variables)

       y: output values (dependent variable)

_R_e_f_e_r_e_n_c_e_s:

     Breiman, Leo (1996) Bagging predictors. Machine Learning 24, pages
     123-140.

     Friedman, Jerome H. (1991) Multivariate adaptive regression
     splines. The Annals of Statistics 19 (1), pages 1-67.

