gbmCMA-methods              package:CMA              R Documentation

_T_r_e_e-_b_a_s_e_d _G_r_a_d_i_e_n_t _B_o_o_s_t_i_n_g

_D_e_s_c_r_i_p_t_i_o_n:

     Roughly speaking, Boosting combines 'weak learners' in a weighted
     manner in a stronger ensemble. This method calls the function
     'gbm.fit' from the package 'gbm'. The 'weak learners' are simple
     trees that need only very few splits (default: 1).

_M_e_t_h_o_d_s:



     _X = "_m_a_t_r_i_x", _y = "_n_u_m_e_r_i_c", _f = "_m_i_s_s_i_n_g" signature 1

     _X = "_m_a_t_r_i_x", _y = "_f_a_c_t_o_r", _f = "_m_i_s_s_i_n_g" signature 2

     _X = "_d_a_t_a._f_r_a_m_e", _y = "_m_i_s_s_i_n_g", _f = "_f_o_r_m_u_l_a" signature 3

     _X = "_E_x_p_r_e_s_s_i_o_n_S_e_t", _y = "_c_h_a_r_a_c_t_e_r", _f = "_m_i_s_s_i_n_g" signature 4

     For further argument and output information, consult 'gbmCMA.'

