Selected model does not contain any target value prior

Question

Hi ODM experts,
I have tried to apply the SVM alg in order to find anomalous records.The table source have rows like that:
uniq_rec ID NAME A1 A2 A3 A4 A5 data
577     2052956018     NAMEHDRCP8     2.27     0.4     85.46     0.01     14.54     24-JAN-13
578     1250914484     NAMEDJDRVP3     11.45     1.24     56.24     0.01     43.77     24-JAN-13
579     1968689283     NAMEDKEND12     0.000011     6.78     0.000029     0.01     0.091     24-JAN-13
580     2063389130     NAMEDNMXG14     0.000011     0.65     36.65     0.02     0.091     24-JAN-13
unq_rec is the pk, id is the id for the generic name and A1 .. A5 attributes ,data when collection occur etc
I'm trying to execute the following code:
drop table ALG_SET;
exec dbms_data_mining.drop_model('SVMODEL');
create table ALG_SET (setting_name varchar2(30), setting_value varchar2(4000));
insert into ALG_SET values ('ALGO_NAME','ALGO_SUPPORT_VECTOR_MACHINES');
insert into ALG_SET values ('PREP_AUTO','ON');
commit;
Begin
dbms_data_mining.create_model('SVMODEL', 'CLASSIFICATION', 'ODM_PAR_FIN_HIST', 'UNQ_CRT', null, 'ALG_SET');
end;
The results is the following error:ORA-40104: invalid training data for model build ( if I run the code) .If I run from graphical interface I have obtained this
error code " Selected model does not contain any target value prior"(using the similar model - SVM for anomaly detction plus the same source table )
Please advice what is missing or wrong and if possible how to bypass this issue.
Thanks in advance for support.
Best Regards,
Bogdan

Answer 1

Here is also a newer example of creating a SVM Anomaly model from ODM sample code (12.1 version but this applies to 11.2):
Rem
Rem $Header: rdbms/demo/dmsvodem.sql /main/6 2012/04/15 16:31:56 xbarr Exp $
Rem
Rem dmsvodem.sql
Rem
Rem Copyright (c) 2004, 2012, Oracle and/or its affiliates.
Rem All rights reserved.
Rem
Rem    NAME
Rem      dmsvodem.sql - Sample program for the DBMS_DATA_MINING package.
Rem
Rem    DESCRIPTION
Rem      This script creates an anomaly detection model
Rem      for data analysis and outlier identification using the
Rem      one-class SVM algorithm
Rem      and data in the SH (Sales History)schema in the RDBMS.
Rem
Rem    NOTES
Rem   
Rem
Rem    MODIFIED   (MM/DD/YY)
Rem    amozes      01/23/12 - updates for 12c
Rem    xbarr       01/10/12 - add prediction_details demo
Rem    ramkrish    06/14/07 - remove commit after settings
Rem    ramkrish    10/25/07 - replace deprecated get_model calls with catalog
Rem                           queries
Rem    ktaylor     07/11/05 - minor edits to comments
Rem    jcjeon      01/18/05 - add column format
Rem    bmilenov    10/28/04 - bmilenov_oneclass_demo
Rem    bmilenov    10/25/04 - Remove dbms_output statements
Rem    bmilenov    10/22/04 - Comment revision
Rem    bmilenov    10/20/04 - Created
Rem
SET serveroutput ON
SET trimspool ON 
SET pages 10000
SET echo ON
--                            SAMPLE PROBLEM
-- Given demographics about a set of customers that are known to have
-- an affinity card, 1) find the most atypical members of this group
-- (outlier identification), 2) discover the common demographic
-- characteristics of the most typical customers with affinity card,
-- and 3) compute how typical a given new/hypothetical customer is.
-- DATA
-- The data for this sample is composed from base tables in the SH schema
-- (See Sample Schema Documentation) and presented through a view:
-- mining_data_one_class_v
-- (See dmsh.sql for view definition).
--                            BUILD THE MODEL
-- Cleanup old model with the same name (if any)
BEGIN DBMS_DATA_MINING.DROP_MODEL('SVMO_SH_Clas_sample');
EXCEPTION WHEN OTHERS THEN NULL; END;
-- PREPARE DATA
-- Automatic data preparation is used.
-- SPECIFY SETTINGS
-- Cleanup old settings table (if any)
BEGIN
  EXECUTE IMMEDIATE 'DROP TABLE svmo_sh_sample_settings';
EXCEPTION WHEN OTHERS THEN
  NULL;
END;
-- CREATE AND POPULATE A SETTINGS TABLE
set echo off
CREATE TABLE svmo_sh_sample_settings (
  setting_name  VARCHAR2(30),
  setting_value VARCHAR2(4000));
set echo on
BEGIN      
  -- Populate settings table
  -- SVM needs to be selected explicitly (default classifier: Naive Bayes)
  -- Examples of other possible overrides are:
  -- select a different rate of outliers in the data (default 0.1)
  -- (dbms_data_mining.svms_outlier_rate, ,0.05);
  -- select a kernel type (default kernel: selected by the algorithm)
  -- (dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_linear);
  -- (dbms_data_mining.svms_kernel_function, dbms_data_mining.svms_gaussian);
  -- turn off active learning (enabled by default)
  -- (dbms_data_mining.svms_active_learning, dbms_data_mining.svms_al_disable);
  INSERT INTO svmo_sh_sample_settings (setting_name, setting_value) VALUES
  (dbms_data_mining.algo_name, dbms_data_mining.algo_support_vector_machines); 
  INSERT INTO svmo_sh_sample_settings (setting_name, setting_value) VALUES
  (dbms_data_mining.prep_auto, dbms_data_mining.prep_auto_on);
END;
-- CREATE A MODEL
-- Build a new one-class SVM Model
-- Note the NULL sprecification for target column name
BEGIN
  DBMS_DATA_MINING.CREATE_MODEL(
    model_name          => 'SVMO_SH_Clas_sample',
    mining_function     => dbms_data_mining.classification,
    data_table_name     => 'mining_data_one_class_v',
    case_id_column_name => 'cust_id',
    target_column_name  => NULL,
    settings_table_name => 'svmo_sh_sample_settings');
END;
-- DISPLAY MODEL SETTINGS
column setting_name format a30
column setting_value format a30
SELECT setting_name, setting_value
  FROM user_mining_model_settings
WHERE model_name = 'SVMO_SH_CLAS_SAMPLE'
ORDER BY setting_name;