tuningresult-class            package:CMA            R Documentation

"_t_u_n_i_n_g_r_e_s_u_l_t"

_D_e_s_c_r_i_p_t_i_o_n:

     Object returned by the function 'tune'

_S_l_o_t_s:


     '_h_y_p_e_r_g_r_i_d': A 'data.frame' representing the grid of values that
          were tried and evaluated. The number of columns equals the
          number of tuned hyperparameters and the number rows equals
          the number of all possible combinations of the discrete
          grids.

     '_t_u_n_e_r_e_s': A list whose lengths equals the number of different
          'learningsets' for which tuning has been performed and whose
          elements are numeric vectors with length equal to the number
          of rows of 'hypergrid' (s.above), containing the
          misclassifcation rate belonging to the respective
          hyperparameter/hyperparameter combination. In order to to get
          an overview about the best hyperparmeter/hyperparameter
          combination, use the convenience method 'best'

     '_m_e_t_h_o_d': Name of the classifier that has been tuned.

     '_f_o_l_d': Number of cross-validation fold used for tuning, s.
          argument of the same name in 'tune'

_M_e_t_h_o_d_s:


     _s_h_o_w Use 'show(tuninresult-object)' for brief information.

     _b_e_s_t Use 'best(tuningresult-object)' to see which
          hyperparameter/hyperparameter combination has performed best
          in terms of the misclassification rate, s.
          'best,tuningresult-method'

     _p_l_o_t Use 'plot(tuningresult-object, iter, which)' to display the
          performance of hyperparameter/hyperparameter combinations
          graphically, either as one-dimensional or as two-dimensional
          (contour) plot, s. 'plot,tuningresult-method'

_A_u_t_h_o_r(_s):

     Martin Slawski martin.slawski@campus.lmu.de

     Anne-Laure Boulesteix <URL: http://www.slcmsr.net/boulesteix>

_S_e_e _A_l_s_o:

     'tune'

