mlbench.friedman1          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 _1

_D_e_s_c_r_i_p_t_i_o_n:

     The regression problem Friedman 1 as described in Friedman (1991)
     and Breiman (1996). Inputs are 10 independent variables uniformly
     distributed on the interval [0,1], only 5 out of these 10 are
     actually used. Outputs are created according to the formula

      y = 10 sin(pi x1 x2) + 20 (x3 - 0.5)^2 + 10 x4 + 5 x5 + e


     where e is N(0,sd).

_U_s_a_g_e:

     mlbench.friedman1(n, sd=1)

_A_r_g_u_m_e_n_t_s:

       n: number of patterns to create

      sd: Standard deviation of noise

_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.

