findFinalClassifier-methods     package:Rmagpie     R Documentation

_f_i_n_d_F_i_n_a_l_C_l_a_s_s_i_f_i_e_r _M_e_t_h_o_d _t_o _t_r_a_i_n _a_n_d _b_u_i_l_d _t_h_e _f_i_n_a_l _c_l_a_s_s_i_f_i_e_r _b_a_s_e_d _o_n _a_n _a_s_s_e_s_s_m_e_n_t

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

     This method generates and stores the final classifier
     corresponding to an assessment. This classifier can then be used
     to classify new samples by calling 'classifyNewSamples'. The final
     classifier is build according to the classifier selected for a
     given assessment, applied on the whole data considering only the
     genes selected by the feature selction method selected.

_V_a_l_u_e:

     The methods returns an object of class assessment which
     finalClassifier has been build.

_M_e_t_h_o_d_s:



     _o_b_j_e_c_t = "_a_s_s_e_s_s_m_e_n_t" This method is only applicable on objects of
          class assessment.

_S_e_e _A_l_s_o:

     'finalClassifier', 'assessment'

_E_x_a_m_p_l_e_s:

     #dataPath <- file.path("C:", "Documents and Settings", "c.maumet", "My Documents", "Programmation", "Sources", "SVN", "R package", "data")
     #aDataset <- new("dataset", dataId="vantVeer_70", dataPath=dataPath)
     #aDataset <- loadData(aDataset)
     data('vV70genesDataset')

     # With the RFE-SVM as feature selection method
     expeOfInterest <- new("assessment", dataset=vV70genes,
                                        noFolds1stLayer=10,
                                        noFolds2ndLayer=9,
                                        classifierName="svm",
                                        typeFoldCreation="original",
                                        svmKernel="linear",
                                        noOfRepeat=2,
                                        featureSelectionOptions=new("geneSubsets", optionValues=c(1,2,4,8,16,32,64,70)))

     # Build the final classifier
     expeOfInterest <- findFinalClassifier(expeOfInterest)

     # With the NSC as feature selection method
     expeOfInterest <- new("assessment", dataset=vV70genes,
                                        noFolds1stLayer=10,
                                        noFolds2ndLayer=9,
                                        featureSelectionMethod="nsc",
                                        classifierName="nsc",
                                        typeFoldCreation="original",
                                        svmKernel="linear",
                                        noOfRepeat=2,
                                        featureSelectionOptions=new("thresholds"))

     # Build the final classifier
     expeOfInterest <- findFinalClassifier(expeOfInterest)

