def predict(self, predict_table_name, actual_label_col=''):
            #Transform the columns if any of them are categorical
            predict_table_name, _indep, _dep, _discard = pivotCategoricalColumns(self.dbconn,predict_table_name, 
                                                                       self.model['indep_org'], 
                                                                       actual_label_col,
                                                                       self.model['col_distinct_vals_dict']
                                                        )
            stmt = '''
                      select *, 
                             {madlib_schema}.array_dot(array{coef}::real[],{indep}) as prediction 
                      from {table_name}
                   '''.format(
                              coef=self.model['coef'],
                              indep=self.model['indep'],
                              table_name=predict_table_name,
                              madlib_schema=self.dbconn.getMADlibSchema()
                             )
            prediction_results = psql.read_frame(stmt,self.dbconn.getConnection())
            return prediction_results
Return predicted values using the trained model. Also return precision, recall & f-measure
dataset/ETHPy150Open pivotalsoftware/pymadlib/pymadlib/pymadlib.py/LinearRegression.predict