def assessModel(self, target: str, prediction: str, nominal: bool = True, event: str = '<STR_LIT>', **kwargs):
<EOL>self.sas.submit("<STR_LIT>")<EOL>objtype = "<STR_LIT>"<EOL>objname = '<STR_LIT>'.format(s=self.table[:<NUM_LIT:3>], n=<NUM_LIT:3>,<EOL>c='<STR_LIT:_>') + self.sas._objcnt()  <EOL>code = "<STR_LIT>"<EOL>score_table = str(self.libref + '<STR_LIT:.>' + self.table)<EOL>binstats = str(objname + '<STR_LIT:.>' + "<STR_LIT>")<EOL>out = str(objname + '<STR_LIT:.>' + "<STR_LIT>")<EOL>level = '<STR_LIT>'<EOL>if nominal:<EOL><INDENT>level = '<STR_LIT:class>'<EOL>try:<EOL><INDENT>if len(event) < <NUM_LIT:1>:<EOL><INDENT>raise Exception(event)<EOL><DEDENT><DEDENT>except Exception:<EOL><INDENT>print("<STR_LIT>")<EOL>event_code = "<STR_LIT>" % (<EOL>self.libref, self.table, self._dsopts())<EOL>event_code += "<STR_LIT>" % target<EOL>event_code += "<STR_LIT>" % target<EOL>ec = self.sas._io.submit(event_code)<EOL>HTML(ec['<STR_LIT>'])<EOL><DEDENT><DEDENT>if nominal:<EOL><INDENT>code += "<STR_LIT>"% (score_table, self._dsopts(), target, prediction, level, binstats, out, event)<EOL><DEDENT>else:<EOL><INDENT>code += "<STR_LIT>"% (score_table, self._dsopts(), target, prediction, level, binstats, out)<EOL><DEDENT>rename_char = """<STR_LIT>"""<EOL>code += rename_char.format(binstats)<EOL>if nominal:<EOL><INDENT>graphics ="""<STR_LIT>"""<EOL>code += graphics.format(out)<EOL><DEDENT>code += "<STR_LIT>"<EOL>code += "<STR_LIT>" % (objname, objtype, self.table)<EOL>if self.sas.nosub:<EOL><INDENT>print(code)<EOL>return<EOL><DEDENT>ll = self.sas.submit(code, '<STR_LIT:text>')<EOL>obj1 = sp2.SASProcCommons._objectmethods(self, objname)<EOL>return sp2.SASresults(obj1, self.sas, objname, self.sas.nosub, ll['<STR_LIT>'])<EOL>
This method will calculate assessment measures using the SAS AA_Model_Eval Macro used for SAS Enterprise Miner.
Not all datasets can be assessed. This is designed for scored data that includes a target and prediction columns
TODO: add code example of build, score, and then assess

:param target: string that represents the target variable in the data
:param prediction: string that represents the numeric prediction column in the data. For nominal targets this should a probability between (0,1).
:param nominal: boolean to indicate if the Target Variable is nominal because the assessment measures are different.
:param event: string which indicates which value of the nominal target variable is the event vs non-event
:param kwargs:
:return: SAS result object