5.2 The CREATE_MODEL Procedure

The CREATE_MODEL procedure in the DBMS_DATA_MINING package uses the specified data to create a mining model with the specified name and mining function. The model can be created with configuration settings and user-specified transformations.

PROCEDURE CREATE_MODEL(
                  model_name            IN VARCHAR2,
                  mining_function       IN VARCHAR2,
                  data_table_name       IN VARCHAR2,
                  case_id_column_name   IN VARCHAR2,
                  target_column_name    IN VARCHAR2 DEFAULT NULL,
                  settings_table_name   IN VARCHAR2 DEFAULT NULL,
                  data_schema_name      IN VARCHAR2 DEFAULT NULL,
                  settings_schema_name  IN VARCHAR2 DEFAULT NULL,
                  xform_list            IN TRANSFORM_LIST DEFAULT NULL);

5.2.1 Choosing the Mining Technique

Explains about providing mining technique to CREATE_MODEL.

The mining technique is a required argument to the CREATE_MODEL procedure. A data mining technique specifies a class of problems that can be modeled and solved.

Data mining techniques implement either supervised or unsupervised learning. Supervised learning uses a set of independent attributes to predict the value of a dependent attribute or target. Unsupervised learning does not distinguish between dependent and independent attributes. Supervised techniques are predictive. Unsupervised techniques are descriptive.

Note:

In data mining terminology, a technique is a general type of problem to be solved by a given approach to data mining. In SQL language terminology, a function is an operator that returns a value.

In Oracle Data Mining documentation, the term technique, or mining technique refers to a data mining technique; the term SQL function or SQL Data Mining function refers to a SQL function for scoring (applying data mining models).

You can specify any of the values in the following table for the mining_function parameter to CREATE_MODEL.

Table 5-2 Mining Model Techniques

Mining_Function Value	Description
ASSOCIATION	Association is a descriptive mining technique. An association model identifies relationships and the probability of their occurrence within a data set. (association rules) Association models use the Apriori algorithm.
ATTRIBUTE_IMPORTANCE	Attribute Importance is a predictive mining technique. An attribute importance model identifies the relative importance of attributes in predicting a given outcome. Attribute Importance models use the Minimum Description Length algorithm and CUR Matrix Decomposition.
CLASSIFICATION	Classification is a predictive mining technique. A classification model uses historical data to predict a categorical target. Classification models can use Naive Bayes, Neural Network, Decision Tree, Logistic Regression, Random Forest, Support Vector Machines, or Explicit Semantic Analysis. The default is Naive Bayes. The classification technique can also be used for anomaly detection. In this case, the SVM algorithm with a null target is used (One-Class SVM).
CLUSTERING	Clustering is a descriptive mining technique. A clustering model identifies natural groupings within a data set. Clustering models can use k-Means, O-Cluster, or Expectation Maximization. The default is k-Means.
FEATURE_EXTRACTION	Feature Extraction is a descriptive mining technique. A feature extraction model creates a set of optimized attributes. Feature extraction models can use Non-Negative Matrix Factorization, Singular Value Decomposition (which can also be used for Principal Component Analysis) or Explicit Semantic Analysis. The default is Non-Negative Matrix Factorization.
REGRESSION	Regression is a predictive mining technique. A regression model uses historical data to predict a numerical target. Regression models can use Support Vector Machines or Linear Regression. The default is Support Vector Machine.
TIME_SERIES	Time series is a predictive mining technique. A time series model forecasts the future values of a time-ordered series of historical numeric data over a user-specified time window. Time series models use the Exponential Smoothing algorithm. The default is Exponential Smoothing.

5.2.2 Choosing the Algorithm

Learn about providing the algorithm settings for a model.

The ALGO_NAME setting specifies the algorithm for a model. If you use the default algorithm for the mining technique, or if there is only one algorithm available for the mining technique, you do not need to specify the ALGO_NAME setting. Instructions for specifying model settings are in "Specifying Model Settings".

Table 5-3 Data Mining Algorithms

ALGO_NAME Value	Algorithm	Default?	Mining Model Technique
ALGO_AI_MDL	Minimum Description Length	—	attribute importance
ALGO_APRIORI_ASSOCIATION_RULES	Apriori	—	association
ALGO_CUR_DECOMPOSITION	CUR Decomposition		Attribute Importance
ALGO_DECISION_TREE	Decision Tree	—	classification
ALGO_EXPECTATION_MAXIMIZATION	Expectation Maximization
ALGO_EXPLICIT_SEMANTIC_ANALYS	Explicit Semantic Analysis	—	feature extraction classification
ALGO_EXPONENTIAL_SMOOTHING	Exponential Smoothing	—	time series
ALGO_EXTENSIBLE_LANG	Language used for extensible algorithm	—	All mining techniques are supported
ALGO_GENERALIZED_LINEAR_MODEL	Generalized Linear Model	—	classification and regression
ALGO_KMEANS	k-Means	yes	clustering
ALGO_NAIVE_BAYES	Naive Bayes	yes	classification
ALGO_NEURAL_NETWORK	Neural Network	—	classification
ALGO_NONNEGATIVE_MATRIX_FACTOR	Non-Negative Matrix Factorization	yes	feature extraction
ALGO_O_CLUSTER	O-Cluster	—	clustering
ALGO_RANDOM_FOREST	Random Forest	—	classification
ALGO_SINGULAR_VALUE_DECOMP	Singular Value Decomposition (can also be used for Principal Component Analysis)	—	feature extraction
ALGO_SUPPORT_VECTOR_MACHINES	Support Vector Machine	yes	default regression algorithm regression, classification, and anomaly detection (classification with no target)

5.2.3 Supplying Transformations

You can optionally specify transformations for the build data in the xform_list parameter to CREATE_MODEL. The transformation instructions are embedded in the model and reapplied whenever the model is applied to new data.

5.2.3.1 Creating a Transformation List

The following are the ways to create a transformation list:

• The STACK interface in DBMS_DATA_MINING_TRANSFORM.

  The STACK interface offers a set of pre-defined transformations that you can apply to an attribute or to a group of attributes. For example, you can specify supervised binning for all categorical attributes.

• The SET_TRANSFORM procedure in DBMS_DATA_MINING_TRANSFORM.

  The SET_TRANSFORM procedure applies a specified SQL expression to a specified attribute. For example, the following statement appends a transformation instruction for country_id to a list of transformations called my_xforms. The transformation instruction divides country_id by 10 before algorithmic processing begins. The reverse transformation multiplies country_id by 10.

    dbms_data_mining_transform.SET_TRANSFORM (my_xforms,
       'country_id', NULL, 'country_id/10', 'country_id*10');
  

  The reverse transformation is applied in the model details. If country_id is the target of a supervised model, the reverse transformation is also applied to the scored target.

5.2.3.2 Transformation List and Automatic Data Preparation

Understand the interaction between transformation list and Automatic Data Preparation (ADP).

The transformation list argument to CREATE_MODEL interacts with the PREP_AUTO setting, which controls ADP:

• When ADP is on and you specify a transformation list, your transformations are applied with the automatic transformations and embedded in the model. The transformations that you specify are executed before the automatic transformations.

• When ADP is off and you specify a transformation list, your transformations are applied and embedded in the model, but no system-generated transformations are performed.

• When ADP is on and you do not specify a transformation list, the system-generated transformations are applied and embedded in the model.

• When ADP is off and you do not specify a transformation list, no transformations are embedded in the model; you must separately prepare the data sets you use for building, testing, and scoring the model.