Before Creating a Model

Explains the preparation steps before creating a model.

Models are database schema objects that perform machine learning. The DBMS_DATA_MINING PL/SQL package is the API for creating, configuring, evaluating, and querying machine learning models (model details).

Before you create a model, you must decide what you want the model to do. You must identify the training data and determine if transformations are required. You can specify model settings to influence the behavior of the model behavior. The preparation steps are summarized in the following table.

Table 3-11 Preparation for Creating an Oracle Machine Learning for SQL Model

Preparation Step	Description
Choose the machine learning function	See Choose the Machine Learning Technique
Choose the algorithm	See Choose the Algorithm
Identify the build (training) data	See Data Preparation
For classification and regression models, identify the test data	See Splitting the Data
Determine your data transformation strategy and create and populate a settings tables (if needed)	See Specify Model Settings

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Choose the Machine Learning Technique

Describes providing an Oracle Machine Learning for SQL machine learning function for the CREATE_MODEL and CREATE_MODEL2 procedure.

An OML4SQL machine learning technique specifies a class of problems that can be modeled and solved. You specify a machine learning with the mining_function argument of the CREATE_MODEL and CREATE_MODEL2 procedure.

OML4SQL machine learning functions 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 functions are predictive. Unsupervised functions are descriptive.

Note:

In OML4SQL terminology, a function is a general type of problem to be solved by a given approach to machine learning. In SQL language terminology, a function is an operation that returns a result.

In OML4SQL documentation, the term function, or machine learning function refers to an OML4SQL machine learning function; the term SQL function or SQL machine learning function refers to a SQL function for scoring (applying machine learning models).

You can specify any of the values in the following table for the mining_function parameter to the CREATE_MODEL and CREATE_MODEL2 procedure.

Table 3-12 Oracle Machine Learning mining_function Values

mining_function Value	Description
`ASSOCIATION`	Association is a descriptive machine learning function. 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 machine learning function. 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 machine learning function. 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 Machine, Explicit Semantic Analysis, or XGBoost. The default is Naive Bayes. You can also specify the classification machine learning function for anomaly detection for a One-Class SVM model and a Multivariate State Estimation Technique - Sequential Probability Ratio Test model.
`CLUSTERING`	Clustering is a descriptive machine learning function. 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 machine learning function. 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 machine learning function. A regression model uses historical data to predict a numerical target. Regression models can use Support Vector Machine, GLM regression, or XGBoost. The default is Support Vector Machine.
`TIME_SERIES`	Time series is a predictive machine learning function. 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.

Choose 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 machine learning technique, or if there is only one algorithm available for the machine learning technique, then you do not need to specify the ALGO_NAME setting.

Table 3-13 Oracle Machine Learning Algorithms

ALGO_NAME Value	Algorithm	Default?	Machine Learning Model Function
`ALGO_AI_MDL`	Minimum Description Length	—	Attribute importance
`ALGO_APRIORI_ASSOCIATION_RULES`	Apriori	—	Association
`ALGO_CUR_DECOMPOSITION`	CUR Matrix Decomposition	—	Attribute importance
`ALGO_DECISION_TREE`	Decision Tree	—	Classification
`ALGO_EXPECTATION_MAXIMIZATION`	Expectation Maximization	—	Clustering and Anomaly Detection
`ALGO_EXPLICIT_SEMANTIC_ANALYS`	Explicit Semantic Analysis	—	Feature extraction and classification
`ALGO_EXPONENTIAL_SMOOTHING`	Exponential Smoothing	—	Time series and time series regression
`ALGO_EXTENSIBLE_LANG`	Language used for an extensible algorithm	—	All machine learning functions are supported
`ALGO_GENERALIZED_LINEAR_MODEL`	Generalized Linear Model	—	Classification and regression
`ALGO_KMEANS`	k-Means	yes	Clustering
`ALGO_MSET_SPRT`	Multivariate State Estimation Technique - Sequential Probability Ratio Test	—	Anomaly detection (classification with no target)
`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)
`ALGO_XGBOOST`	XGBoost	—	Classification and regression

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