4 Scoring and Deployment

In-Database Scoring

Scoring, the application of models to new data, is a primary objective of data mining. Once the models have been built, the challenges come in deploying them to obtain the best results, and in maintaining them within a production environment.

In traditional data mining, models are built using specialized software on a remote system and deployed to another system for scoring. This is a cumbersome, error-prone process open to security violations and difficulties in data synchronization.

With Oracle Data Mining, scoring is easy and secure. The scoring engine and the data both reside within the database. Scoring is an extension to the SQL language, so the results of mining can easily be incorporated into applications and reporting systems.

In-database mining provides security, backup and recovery, and high performance. It minimizes the IT effort needed to support data mining initiatives. Using standard database techniques, models can easily be refreshed (recreated) on more recent data and redeployed. The deployment is immediate since the scoring query remains the same; only the underlying model is replaced in the database.

What is Deployment?

Deploying a model means using it within a target environment. Model deployment could be:

• Scoring data either for batch or real-time results

• Extracting model details to produce reports. For example: the rules from a Decision Tree model, or the attribute rankings from an Attribute Importance model

• Extending the business intelligence infrastructure of a data warehouse by incorporating mining results in applications or operational systems

• Moving a model from the database where it was built to the database where it will be used for scoring (export/import)

Real-Time Scoring

Oracle Data Mining SQL functions enable prediction, clustering, and feature extraction analysis to be easily integrated into live production and operational systems. Because mining results are returned within SQL queries, mining can occur in real time.

With real-time scoring, point-of-sales database transactions can be mined. Predictions and rule sets can be generated to help front-line workers make better analytical decisions. Real-time scoring enables fraud detection, identification of potential liabilities, and recognition of better marketing and selling opportunities.

The query in Example 4-1 uses a Decision Tree model named dt_sh_clas_sample to predict the probability that customer 101488 will use an affinity card. A customer representative could retrieve this information in real time when talking to this customer on the phone. Based on the query result, the representative might offer an extra-value card, since there is a 73% chance that the customer will use a card.

SELECT PREDICTION_PROBABILITY(dt_sh_clas_sample, 1 USING *) cust_card_prob
       FROM mining_data_apply_v
       WHERE cust_id = 101488;

CUST_CARD_PROB--------------    .727642276

UPDATE CUST_RESPONSE_APPLY_UPDATE
SET prediction = prediction(CUST_RESPONSE19964_DT using *),
     probability =  prediction_probability(CUST_RESPONSE19964_DT using *)

Cost-Sensitive Decision Making

Costs are user-specified numbers that bias classification. The algorithm uses positive numbers to penalize more expensive outcomes over less expensive outcomes. Higher numbers indicate higher costs. The algorithm uses negative numbers to favor more beneficial outcomes over less beneficial outcomes. Lower negative numbers indicate higher benefits.

All classification algorithms can use costs for scoring. You can specify the costs in a cost matrix table, or you can specify the costs inline when scoring. The PREDICTION, PREDICTION_COST, and PREDICTION_SET functions all support costs.

A sample cost matrix table is shown in Table 4-1.

The cost matrix in Table 4-1 specifies costs for a binary target. The matrix indicates that the algorithm should treat a misclassified 0 as twice as costly as a misclassified 1. If the table name is cost_tbl and it is associated with the Naive Bayes model nb_sh_clas_sample, then the following query takes cost_tbl into account when scoring nb_sh_clas_sample. The output will be restricted to those rows where a prediction of 1 is less costly then a prediction of 0.

SELECT cust_gender, COUNT(*) AS cnt, ROUND(AVG(age)) AS avg_age
   FROM mining_data_apply_v
   WHERE PREDICTION (nb_sh_clas_sample COST MODEL
      USING cust_marital_status, education, household_size) = 1
   GROUP BY cust_gender
   ORDER BY cust_gender;

If there is a possibility that the cost matrix table is not present, or that a cost matrix was not specified for the model, you can use the AUTO keyword with COST MODEL so that scoring only uses costs if the cost matrix is available.

SELECT cust_gender, COUNT(*) AS cnt, ROUND(AVG(age)) AS avg_age
   FROM mining_data_apply_v
   WHERE PREDICTION (nb_sh_clas_sample COST MODEL AUTO
      USING cust_marital_status, education, household_size) = 1
   GROUP BY cust_gender
   ORDER BY cust_gender;

You can specify the costs inline when you invoke the scoring function. The inline costs are used for scoring even if a cost matrix table is associated with the model. Here is the same query with the costs specified inline.

SELECT cust_gender, COUNT(*) AS cnt, ROUND(AVG(age)) AS avg_age
   FROM mining_data_apply_v
   WHERE PREDICTION (nb_sh_clas_sample
                     COST (0,1) values ((0, 2),
                                        (1, 0))
                     USING cust_marital_status, education, household_size) = 1
   GROUP BY cust_gender
   ORDER BY cust_gender;

To associate a cost matrix table with a model for scoring, use the ADD_COST_MATRIX procedure in the DBMS_DATA_MINING package. You can retrieve the cost matrix with the GET_COST_MATRIX function. The REMOVE_COST_MATRIX procedure removes the cost matrix. If you want to use a different cost matrix table with a model, first remove the existing one then add the new one.

-- add cost matrix table cost_tbl
-- to model nb_sh_clas_sample
-- for scoring
--
EXEC DBMS_DATA_MINING.ADD_COST_MATRIX('nb_sh_clas_sample', 'cost_tbl');
--
-- replace cost_tbl with cost_tbl_2
--
EXEC DBMS_DATA_MINING.REMOVE_COST_MATRIX('nb_sh_clas_sample', 'cost_tbl');
EXEC DBMS_DATA_MINING.ADD_COST_MATRIX('nb_sh_clas_sample', 'cost_tbl_2');

The Decision Tree algorithm can use costs to bias the model build. If you want to create a Decision Tree model with costs, create a cost matrix table and provide its name in the CLAS_COST_TABLE_NAME setting for the model. If you specify costs when scoring the model, the cost matrix used to create the model will be used. If you want to use a different cost matrix table for scoring, first remove the existing cost matrix table then add the new one.

Batch Apply

Oracle Data Mining supports a batch apply operation that writes the results of scoring directly to a table. The columns in the table are mining function-dependent. The apply operation is accomplished by DBMS_DATA_MINING.APPLY.

APPLY creates an output table with the columns shown in Table 4-2.

Example 4-2 illustrates anomaly detection with APPLY. The query of the APPLY output table returns the ten first customers in the table. Each has a a probability for being typical (1) and a probability for being anomalous (0).

EXEC dbms_data_mining.apply
        ('SVMO_SH_Clas_sample','svmo_sh_sample_prepared', 
         'cust_id', 'one_class_output'); 

SELECT * from one_class_output where rownum < 11;
 
   CUST_ID PREDICTION PROBABILITY
---------- ---------- -----------
    101798          1  .567389309
    101798          0  .432610691
    102276          1  .564922469
    102276          0  .435077531
    102404          1   .51213544
    102404          0   .48786456
    101891          1  .563474346
    101891          0  .436525654
    102815          0  .500663683
    102815          1  .499336317